{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T18:55:00Z","timestamp":1792522500620,"version":"4.5.0"},"reference-count":383,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>Graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques have been proposed for generating effective graph representation vectors, which generally fall into two categories: traditional graph embedding methods and graph neural network (GNN)\u2013based methods. These methods can be applied to both static and dynamic graphs. A static graph is a single fixed graph, whereas a dynamic graph evolves over time and its nodes and edges can be added or deleted from the graph. In this survey, we review the graph-embedding methods in both traditional and GNN-based categories for both static and dynamic graphs and include the recent papers published until the time of submission. In addition, we summarize a number of limitations of GNNs and the proposed solutions to these limitations. Such a summary has not been provided in previous surveys. Finally, we explore some open and ongoing research directions for future work.<\/jats:p>","DOI":"10.1145\/3633518","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T12:15:48Z","timestamp":1701173748000},"page":"1-55","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":221,"title":["A Survey on Graph Representation Learning Methods"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6384-4169","authenticated-orcid":false,"given":"Shima","family":"Khoshraftar","sequence":"first","affiliation":[{"name":"Electrical Engineering and Computer Science Department, York University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1765-5751","authenticated-orcid":false,"given":"Aijun","family":"An","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science Department, York University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"Application of network link prediction in drug discovery","volume":"22","author":"Abbas Khushnood","year":"2021","unstructured":"Khushnood Abbas, Alireza Abbasi, Shi Dong, Ling Niu, Laihang Yu, Bolun Chen, Shi-Min Cai, and Qambar Hasan. 2021. Application of network link prediction in drug discovery. BMC Bioinformatics 22 (2021), 1\u201321.","journal-title":"BMC Bioinformatics"},{"key":"e_1_3_2_3_2","article-title":"Watch your step: Learning node embeddings via graph attention","volume":"31","author":"Abu-El-Haija Sami","year":"2018","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, and Alexander A. Alemi. 2018. Watch your step: Learning node embeddings via graph attention. Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/2488388.2488393","volume-title":"Proceedings of the 22nd International Conference on World Wide Web","author":"Ahmed Amr","year":"2013","unstructured":"Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J. Smola. 2013. Distributed large-scale natural graph factorization. In Proceedings of the 22nd International Conference on World Wide Web. 37\u201348."},{"key":"e_1_3_2_5_2","first-page":"1","volume-title":"2015 IEEE International Conference on Data Mining","author":"Ahmed Nesreen K.","year":"2015","unstructured":"Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, and Nick Duffield. 2015. Efficient graphlet counting for large networks. In 2015 IEEE International Conference on Data Mining. IEEE, 1\u201310."},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/3308558.3313668","volume-title":"The World Wide Web Conference","author":"Al-Rfou Rami","year":"2019","unstructured":"Rami Al-Rfou, Bryan Perozzi, and Dustin Zelle. 2019. DDGK: Learning graph representations for deep divergence graph kernels. In The World Wide Web Conference. 37\u201348."},{"key":"e_1_3_2_7_2","first-page":"982","volume-title":"2021 IEEE International Conference on Data Mining (ICDM)","author":"Ali Muhammad Afif","year":"2021","unstructured":"Muhammad Afif Ali, Suriya Venkatesan, Victor Liang, and Hannes Kruppa. 2021. TEST-GCN: Topologically enhanced spatial-temporal graph convolutional networks for traffic forecasting. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 982\u2013987."},{"key":"e_1_3_2_8_2","volume-title":"International Conference on Learning Representations","author":"Alon Uri","year":"2020","unstructured":"Uri Alon and Eran Yahav. 2020. On the bottleneck of graph neural networks and its practical implications. In International Conference on Learning Representations."},{"key":"e_1_3_2_9_2","first-page":"8017","article-title":"Subgraph neural networks","volume":"33","author":"Alsentzer Emily","year":"2020","unstructured":"Emily Alsentzer, Samuel Finlayson, Michelle Li, and Marinka Zitnik. 2020. Subgraph neural networks. Advances in Neural Information Processing Systems 33 (2020), 8017\u20138029.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"100130","DOI":"10.1016\/j.mlwa.2021.100130","article-title":"Network representation learning systematic review: Ancestors and current development state","volume":"6","author":"Amara Amina","year":"2021","unstructured":"Amina Amara, Mohamed Ali Hadj Taieb, and Mohamed Ben Aouicha. 2021. Network representation learning systematic review: Ancestors and current development state. Machine Learning with Applications 6 (2021), 100130.","journal-title":"Machine Learning with Applications"},{"key":"e_1_3_2_11_2","volume-title":"International Conference on Learning Representations","author":"Azizian Waiss","year":"2020","unstructured":"Waiss Azizian et\u00a0al. 2020. Expressive power of invariant and equivariant graph neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"107637","DOI":"10.1016\/j.patcog.2020.107637","article-title":"Hypergraph convolution and hypergraph attention","volume":"110","author":"Bai Song","year":"2021","unstructured":"Song Bai, Feihu Zhang, and Philip H. S. Torr. 2021. Hypergraph convolution and hypergraph attention. Pattern Recognition 110 (2021), 107637.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_13_2","first-page":"599","volume-title":"International Conference on Machine Learning","author":"Balcilar Muhammet","year":"2021","unstructured":"Muhammet Balcilar, Pierre H\u00e9roux, Benoit Gauzere, Pascal Vasseur, S\u00e9bastien Adam, and Paul Honeine. 2021. Breaking the limits of message passing graph neural networks. In International Conference on Machine Learning. PMLR, 599\u2013608."},{"key":"e_1_3_2_14_2","article-title":"Graph neural networks with local graph parameters","volume":"34","author":"Barcel\u00f3 Pablo","year":"2021","unstructured":"Pablo Barcel\u00f3, Floris Geerts, Juan Reutter, and Maksimilian Ryschkov. 2021. Graph neural networks with local graph parameters. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3483595","article-title":"A survey on embedding dynamic graphs","volume":"55","author":"Barros Claudio D. T.","year":"2021","unstructured":"Claudio D. T. Barros, Matheus R. F. Mendon\u00e7a, Alex B. Vieira, and Artur Ziviani. 2021. A survey on embedding dynamic graphs. ACM Computing Surveys (CSUR) 55, 1 (2021), 1\u201337.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_16_2","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia Peter W.","year":"2018","unstructured":"Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et\u00a0al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018).","journal-title":"arXiv preprint arXiv:1806.01261"},{"key":"e_1_3_2_17_2","first-page":"748","volume-title":"International Conference on Machine Learning","author":"Beaini Dominique","year":"2021","unstructured":"Dominique Beaini, Saro Passaro, Vincent L\u00e9tourneau, Will Hamilton, Gabriele Corso, and Pietro Li\u00f2. 2021. Directional graph networks. In International Conference on Machine Learning. PMLR, 748\u2013758."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1145\/3340531.3411953","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Beladev Moran","year":"2020","unstructured":"Moran Beladev, Lior Rokach, Gilad Katz, Ido Guy, and Kira Radinsky. 2020. tdGraphEmbed: Temporal dynamic graph-level embedding. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 55\u201364."},{"key":"e_1_3_2_19_2","first-page":"585","volume-title":"NIPS","author":"Belkin Mikhail","year":"2001","unstructured":"Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, Vol. 14. 585\u2013591."},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"104933","DOI":"10.1016\/j.compbiomed.2021.104933","article-title":"Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding","volume":"138","author":"Berahmand Kamal","year":"2021","unstructured":"Kamal Berahmand, Elahe Nasiri, Yuefeng Li, et\u00a0al. 2021. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding. Computers in Biology and Medicine 138 (2021), 104933.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_2_21_2","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume":"32","author":"Berthelot David","year":"2019","unstructured":"David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A. Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1145\/3336191.3371800","volume-title":"Proceedings of the 13th International Conference on Web Search and Data Mining","author":"Bhowmick Ayan Kumar","year":"2020","unstructured":"Ayan Kumar Bhowmick, Koushik Meneni, Maximilien Danisch, Jean-Loup Guillaume, and Bivas Mitra. 2020. Louvainne: Hierarchical Louvain method for high quality and scalable network embedding. In Proceedings of the 13th International Conference on Web Search and Data Mining. 43\u201351."},{"key":"e_1_3_2_23_2","first-page":"1026","volume-title":"International Conference on Machine Learning","author":"Bodnar Cristian","year":"2021","unstructured":"Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montufar, Pietro Lio, and Michael Bronstein. 2021. Weisfeiler and Lehman go topological: Message passing simplicial networks. In International Conference on Machine Learning. PMLR, 1026\u20131037."},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1145\/3394486.3403296","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Bojchevski Aleksandar","year":"2020","unstructured":"Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek R\u00f3zemberczki, Michal Lukasik, and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate PageRank. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2464\u20132473."},{"key":"e_1_3_2_25_2","article-title":"Improving graph neural network expressivity via subgraph isomorphism counting","author":"Bouritsas Giorgos","year":"2022","unstructured":"Giorgos Bouritsas, Fabrizio Frasca, Stefanos P. Zafeiriou, and Michael Bronstein. 2022. Improving graph neural network expressivity via subgraph isomorphism counting. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_26_2","volume-title":"International Conference on Learning Representations","author":"Brandstetter Johannes","year":"2022","unstructured":"Johannes Brandstetter, Daniel E. Worrall, and Max Welling. 2022. Message passing neural PDE solvers. In International Conference on Learning Representations."},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1145\/3308558.3313595","volume-title":"The World Wide Web Conference","author":"Brochier Robin","year":"2019","unstructured":"Robin Brochier, Adrien Guille, and Julien Velcin. 2019. Global vectors for node representations. In The World Wide Web Conference. 2587\u20132593."},{"key":"e_1_3_2_28_2","first-page":"1144","volume-title":"International Conference on Machine Learning","author":"Brockschmidt Marc","year":"2020","unstructured":"Marc Brockschmidt. 2020. GNN-FiLM: Graph neural networks with feature-wise linear modulation. In International Conference on Machine Learning. PMLR, 1144\u20131152."},{"key":"e_1_3_2_29_2","volume-title":"2nd International Conference on Learning Representations (ICLR 2014)","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and deep locally connected networks on graphs. In 2nd International Conference on Learning Representations (ICLR 2014)."},{"issue":"9","key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","volume":"30","author":"Cai Hongyun","year":"2018","unstructured":"Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering 30, 9 (2018), 1616\u20131637.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_31_2","first-page":"891","volume-title":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","author":"Cao Shaosheng","year":"2015","unstructured":"Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. GraRep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 891\u2013900."},{"key":"e_1_3_2_32_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"30","author":"Cao Shaosheng","year":"2016","unstructured":"Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30."},{"key":"e_1_3_2_33_2","first-page":"1407","volume-title":"International Conference on Machine Learning","author":"Chamberlain Ben","year":"2021","unstructured":"Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael Bronstein, Stefan Webb, and Emanuele Rossi. 2021. GRAND: Graph neural diffusion. In International Conference on Machine Learning. PMLR, 1407\u20131418."},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1145\/3404835.3462968","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chang Jianxin","year":"2021","unstructured":"Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021. Sequential recommendation with graph neural networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 378\u2013387."},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1145\/3394486.3403185","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Chanpuriya Sudhanshu","year":"2020","unstructured":"Sudhanshu Chanpuriya and Cameron Musco. 2020. InfiniteWalk: Deep network embeddings as Laplacian embeddings with a nonlinearity. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1325\u20131333."},{"key":"e_1_3_2_36_2","first-page":"13185","article-title":"Node embeddings and exact low-rank representations of complex networks","volume":"33","author":"Chanpuriya Sudhanshu","year":"2020","unstructured":"Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis. 2020. Node embeddings and exact low-rank representations of complex networks. Advances in Neural Information Processing Systems 33 (2020), 13185\u201313198.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_37_2","first-page":"485","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Chen Cen","year":"2019","unstructured":"Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, and Zeng Zeng. 2019. Gated residual recurrent graph neural networks for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 485\u2013492."},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1137\/1.9781611974010.63","volume-title":"Proceedings of the 2015 SIAM International Conference on Data Mining","author":"Chen Chen","year":"2015","unstructured":"Chen Chen and Hanghang Tong. 2015. Fast eigen-functions tracking on dynamic graphs. In Proceedings of the 2015 SIAM International Conference on Data Mining. SIAM, 559\u2013567."},{"key":"e_1_3_2_39_2","article-title":"Graph representation learning: A survey","volume":"9","author":"Chen Fenxiao","year":"2020","unstructured":"Fenxiao Chen, Yun-Cheng Wang, Bin Wang, and C.-C. Jay Kuo. 2020. Graph representation learning: A survey. APSIPA Transactions on Signal and Information Processing 9 (2020).","journal-title":"APSIPA Transactions on Signal and Information Processing"},{"key":"e_1_3_2_40_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Chen Haochen","year":"2018","unstructured":"Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena. 2018. HARP: Hierarchical representation learning for networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1145\/3357384.3357879","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Chen Haochen","year":"2019","unstructured":"Haochen Chen, Syed Fahad Sultan, Yingtao Tian, Muhao Chen, and Steven Skiena. 2019. Fast and accurate network embeddings via very sparse random projection. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 399\u2013408."},{"issue":"2","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1109\/TKDE.2020.2982878","article-title":"Social boosted recommendation with folded bipartite network embedding","volume":"34","author":"Chen Hongxu","year":"2020","unstructured":"Hongxu Chen, Hongzhi Yin, Tong Chen, Weiqing Wang, Xue Li, and Xia Hu. 2020. Social boosted recommendation with folded bipartite network embedding. IEEE Transactions on Knowledge and Data Engineering 34, 2 (2020), 914\u2013926.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1145\/3394486.3403201","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Chen Hongxu","year":"2020","unstructured":"Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, and Katarzyna Musial. 2020. Multi-level graph convolutional networks for cross-platform anchor link prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1503\u20131511."},{"key":"e_1_3_2_44_2","volume-title":"International Conference on Learning Representations","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. In International Conference on Learning Representations."},{"key":"e_1_3_2_45_2","first-page":"942","volume-title":"International Conference on Machine Learning","author":"Chen Jianfei","year":"2018","unstructured":"Jianfei Chen, Jun Zhu, and Le Song. 2018. Stochastic training of graph convolutional networks with variance reduction. In International Conference on Machine Learning. PMLR, 942\u2013950."},{"key":"e_1_3_2_46_2","first-page":"14556","article-title":"Scalable graph neural networks via bidirectional propagation","volume":"33","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, and Ji-Rong Wen. 2020. Scalable graph neural networks via bidirectional propagation. Advances in Neural Information Processing Systems 33 (2020), 14556\u201314566.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_47_2","first-page":"1725","volume-title":"International Conference on Machine Learning","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In International Conference on Machine Learning. PMLR, 1725\u20131735."},{"key":"e_1_3_2_48_2","first-page":"1695","volume-title":"International Conference on Machine Learning","author":"Chen Tianlong","year":"2021","unstructured":"Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. 2021. A unified lottery ticket hypothesis for graph neural networks. In International Conference on Machine Learning. PMLR, 1695\u20131706."},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/ICDM50108.2020.00108","volume-title":"2020 IEEE International Conference on Data Mining (ICDM)","author":"Chen Xu","year":"2020","unstructured":"Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang, Yi Ren, Kaigui Bian, and Kunqing Xie. 2020. TSSRGCN: Temporal spectral spatial retrieval graph convolutional network for traffic flow forecasting. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 954\u2013959."},{"key":"e_1_3_2_50_2","first-page":"10383","article-title":"Can graph neural networks count substructures?","volume":"33","author":"Chen Zhengdao","year":"2020","unstructured":"Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna. 2020. Can graph neural networks count substructures? Advances in Neural Information Processing Systems 33 (2020), 10383\u201310395.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_51_2","article-title":"On the equivalence between graph isomorphism testing and function approximation with GNNs","volume":"32","author":"Chen Zhengdao","year":"2019","unstructured":"Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna. 2019. On the equivalence between graph isomorphism testing and function approximation with GNNs. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1145\/3292500.3330925","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Chiang Wei-Lin","year":"2019","unstructured":"Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 257\u2013266."},{"key":"e_1_3_2_53_2","volume-title":"International Conference on Learning Representations","author":"Chien Eli","year":"2020","unstructured":"Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2020. Adaptive universal generalized PageRank graph neural network. In International Conference on Learning Representations."},{"issue":"23","key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"4501","DOI":"10.1093\/bioinformatics\/btab464","article-title":"Node similarity-based graph convolution for link prediction in biological networks","volume":"37","author":"Co\u015fkun Mustafa","year":"2021","unstructured":"Mustafa Co\u015fkun and Mehmet Koyut\u00fcrk. 2021. Node similarity-based graph convolution for link prediction in biological networks. Bioinformatics 37, 23 (2021), 4501\u20134508.","journal-title":"Bioinformatics"},{"key":"e_1_3_2_55_2","first-page":"1","volume-title":"The 2012 International Joint Conference on Neural Networks (IJCNN)","author":"Soares Paulo Ricardo da Silva","year":"2012","unstructured":"Paulo Ricardo da Silva Soares and Ricardo Bastos Cavalcante Prud\u00eancio. 2012. Time series based link prediction. In The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, 1\u20137."},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","first-page":"3074","DOI":"10.1145\/3394486.3403358","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Dai Rui","year":"2020","unstructured":"Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, and Kaikui Liu. 2020. Hybrid spatio-temporal graph convolutional network: Improving traffic prediction with navigation data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3074\u20133082."},{"key":"e_1_3_2_57_2","first-page":"945","volume-title":"Companion Proceedings of the The Web Conference 2018","author":"Dalmia Ayushi","year":"2018","unstructured":"Ayushi Dalmia and Manish Gupta. 2018. Towards interpretation of node embeddings. In Companion Proceedings of the The Web Conference 2018. 945\u2013952."},{"issue":"1","key":"e_1_3_2_58_2","first-page":"1","article-title":"A review of some techniques for inclusion of domain-knowledge into deep neural networks","volume":"12","author":"Dash Tirtharaj","year":"2022","unstructured":"Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, and Ashwin Srinivasan. 2022. A review of some techniques for inclusion of domain-knowledge into deep neural networks. Scientific Reports 12, 1 (2022), 1\u201315.","journal-title":"Scientific Reports"},{"issue":"2","key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s10994-021-06090-8","article-title":"Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment","volume":"111","author":"Dash Tirtharaj","year":"2022","unstructured":"Tirtharaj Dash, Ashwin Srinivasan, and A. Baskar. 2022. Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment. Machine Learning 111, 2 (2022), 575\u2013623.","journal-title":"Machine Learning"},{"issue":"7","key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1007\/s10994-021-05966-z","article-title":"Incorporating symbolic domain knowledge into graph neural networks","volume":"110","author":"Dash Tirtharaj","year":"2021","unstructured":"Tirtharaj Dash, Ashwin Srinivasan, and Lovekesh Vig. 2021. Incorporating symbolic domain knowledge into graph neural networks. Machine Learning 110, 7 (2021), 1609\u20131636.","journal-title":"Machine Learning"},{"key":"e_1_3_2_61_2","first-page":"2126","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"Dasoulas George","year":"2021","unstructured":"George Dasoulas, Ludovic Dos Santos, Kevin Scaman, and Aladin Virmaux. 2021. Coloring graph neural networks for node disambiguation. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 2126\u20132132."},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1145\/3459637.3481916","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Derrow-Pinion Austin","year":"2021","unstructured":"Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, et\u00a0al. 2021. ETA prediction with graph neural networks in Google Maps. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3767\u20133776."},{"key":"e_1_3_2_63_2","first-page":"890","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Diao Zulong","year":"2019","unstructured":"Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He. 2019. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 890\u2013897."},{"key":"e_1_3_2_64_2","article-title":"VQ-GNN: A universal framework to scale up graph neural networks using vector quantization","volume":"34","author":"Ding Mucong","year":"2021","unstructured":"Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, and Tom Goldstein. 2021. VQ-GNN: A universal framework to scale up graph neural networks using vector quantization. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_65_2","article-title":"Spatio-temporal recurrent networks for event-based optical flow estimation","author":"Ding Ziluo","year":"2022","unstructured":"Ziluo Ding, Rui Zhao, Jiyuan Zhang, Tianxiao Gao, Ruiqin Xiong, Zhaofei Yu, and Tiejun Huang. 2022. Spatio-temporal recurrent networks for event-based optical flow estimation. Proceedings of the AAAI Conference on Artificial Intelligence (2022).","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1145\/3097983.3098036","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Dong Yuxiao","year":"2017","unstructured":"Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 135\u2013144."},{"key":"e_1_3_2_67_2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1145\/3459637.3482226","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Dong Yushun","year":"2021","unstructured":"Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, and Jundong Li. 2021. AdaGNN: Graph neural networks with adaptive frequency response filter. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 392\u2013401."},{"key":"e_1_3_2_68_2","first-page":"2079","volume-title":"IJCAI","author":"Du Lun","year":"2018","unstructured":"Lun Du, Zhicong Lu, Yun Wang, Guojie Song, Yiming Wang, and Wei Chen. 2018. Galaxy network embedding: A hierarchical community structure preserving approach. In IJCAI. 2079\u20132085."},{"key":"e_1_3_2_69_2","first-page":"1550","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Du Lun","year":"2022","unstructured":"Lun Du, Xiaozhou Shi, Qiang Fu, Xiaojun Ma, Hengyu Liu, Shi Han, and Dongmei Zhang. 2022. GBK-GNN: Gated bi-kernel graph neural networks for modeling both homophily and heterophily. In Proceedings of the ACM Web Conference 2022. 1550\u20131558."},{"key":"e_1_3_2_70_2","first-page":"2086","volume-title":"IJCAI","author":"Du Lun","year":"2018","unstructured":"Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang. 2018. Dynamic network embedding: An extended approach for skip-gram based network embedding. In IJCAI, Vol. 2018. 2086\u20132092."},{"issue":"2","key":"e_1_3_2_71_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1921632.1921636","article-title":"Temporal link prediction using matrix and tensor factorizations","volume":"5","author":"Dunlavy Daniel M.","year":"2011","unstructured":"Daniel M. Dunlavy, Tamara G. Kolda, and Evrim Acar. 2011. Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data (TKDD) 5, 2 (2011), 1\u201327.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"3","key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1137\/20M1358517","article-title":"Implicit deep learning","volume":"3","author":"Ghaoui Laurent El","year":"2021","unstructured":"Laurent El Ghaoui, Fangda Gu, Bertrand Travacca, Armin Askari, and Alicia Tsai. 2021. Implicit deep learning. SIAM Journal on Mathematics of Data Science 3, 3 (2021), 930\u2013958.","journal-title":"SIAM Journal on Mathematics of Data Science"},{"key":"e_1_3_2_73_2","article-title":"PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations","volume":"34","author":"Eliasof Moshe","year":"2021","unstructured":"Moshe Eliasof, Eldad Haber, and Eran Treister. 2021. PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1145\/3308558.3313660","volume-title":"The World Wide Web Conference","author":"Epasto Alessandro","year":"2019","unstructured":"Alessandro Epasto and Bryan Perozzi. 2019. Is a single embedding enough? learning node representations that capture multiple social contexts. In The World Wide Web Conference. 394\u2013404."},{"key":"e_1_3_2_75_2","first-page":"2286","volume-title":"IJCAI","author":"Fang Shen","year":"2019","unstructured":"Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2019. GSTNet: Global spatial-temporal network for traffic flow prediction. In IJCAI. 2286\u20132293."},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1145\/3394486.3403320","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Fang Xiaomin","year":"2020","unstructured":"Xiaomin Fang, Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, and Haifeng Wang. 2020. ConSTGAT: Contextual spatial-temporal graph attention network for travel time estimation at Baidu maps. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2697\u20132705."},{"key":"e_1_3_2_77_2","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1145\/3485447.3512187","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Fang Zheng","year":"2022","unstructured":"Zheng Fang, Lingjun Xu, Guojie Song, Qingqing Long, and Yingxue Zhang. 2022. Polarized graph neural networks. In Proceedings of the ACM Web Conference 2022. 1404\u20131413."},{"key":"e_1_3_2_78_2","first-page":"22092","article-title":"Graph random neural networks for semi-supervised learning on graphs","volume":"33","author":"Feng Wenzheng","year":"2020","unstructured":"Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph random neural networks for semi-supervised learning on graphs. Advances in Neural Information Processing Systems 33 (2020), 22092\u201322103.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_79_2","volume-title":"International Conferences on Machine Learning (ICML)","author":"Fey Matthias","year":"2021","unstructured":"Matthias Fey, Jan E. Lenssen, Frank Weichert, and Jure Leskovec. 2021. GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings. In International Conferences on Machine Learning (ICML)."},{"key":"e_1_3_2_80_2","first-page":"2273","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Fu Dongqi","year":"2021","unstructured":"Dongqi Fu and Jingrui He. 2021. SDG: A simplified and dynamic graph neural network. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2273\u20132277."},{"key":"e_1_3_2_81_2","first-page":"2331","volume-title":"Proceedings of The Web Conference 2020","author":"Fu Xinyu","year":"2020","unstructured":"Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of The Web Conference 2020. 2331\u20132341."},{"key":"e_1_3_2_82_2","volume-title":"27th International Joint Conference on Artificial Intelligence (IJCAI))","author":"Gao Hongchang","year":"2018","unstructured":"Hongchang Gao and Heng Huang. 2018. Deep attributed network embedding. In 27th International Joint Conference on Artificial Intelligence (IJCAI))."},{"key":"e_1_3_2_83_2","first-page":"1403","volume-title":"IJCAI","author":"Gao Yang","year":"2020","unstructured":"Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graph neural architecture search.. In IJCAI, Vol. 20. 1403\u20131409."},{"key":"e_1_3_2_84_2","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1145\/3292500.3330717","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Geng Yangli-ao","year":"2019","unstructured":"Yangli-ao Geng, Qingyong Li, Tianyang Lin, Lei Jiang, Liangtao Xu, Dong Zheng, Wen Yao, Weitao Lyu, and Yijun Zhang. 2019. LightNet: A dual spatiotemporal encoder network model for lightning prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2439\u20132447."},{"key":"e_1_3_2_85_2","doi-asserted-by":"crossref","first-page":"22106","DOI":"10.1109\/ACCESS.2021.3055280","article-title":"Graph deep learning: State of the art and challenges","volume":"9","author":"Georgousis Stavros","year":"2021","unstructured":"Stavros Georgousis, Michael P. Kenning, and Xianghua Xie. 2021. Graph deep learning: State of the art and challenges. IEEE Access 9 (2021), 22106\u201322140.","journal-title":"IEEE Access"},{"issue":"1","key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s13278-023-01058-z","article-title":"A survey on bipartite graphs embedding","volume":"13","author":"Giamphy Edward","year":"2023","unstructured":"Edward Giamphy, Jean-Loup Guillaume, Antoine Doucet, and Kevin Sanchis. 2023. A survey on bipartite graphs embedding. Social Network Analysis and Mining 13, 1 (2023), 54.","journal-title":"Social Network Analysis and Mining"},{"key":"e_1_3_2_87_2","first-page":"1263","volume-title":"International Conference on Machine Learning","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning. PMLR, 1263\u20131272."},{"key":"e_1_3_2_88_2","article-title":"On the interpretability and evaluation of graph representation learning","author":"Gogoglou Antonia","year":"2019","unstructured":"Antonia Gogoglou, C. Bayan Bruss, and Keegan E. Hines. 2019. On the interpretability and evaluation of graph representation learning. arXiv preprint arXiv:1910.03081 (2019).","journal-title":"arXiv preprint arXiv:1910.03081"},{"key":"e_1_3_2_89_2","article-title":"The reversible residual network: Backpropagation without storing activations","volume":"30","author":"Gomez Aidan N.","year":"2017","unstructured":"Aidan N. Gomez, Mengye Ren, Raquel Urtasun, and Roger B. Grosse. 2017. The reversible residual network: Backpropagation without storing activations. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_90_2","doi-asserted-by":"crossref","first-page":"104816","DOI":"10.1016\/j.knosys.2019.06.024","article-title":"dyngraph2vec: Capturing network dynamics using dynamic graph representation learning","volume":"187","author":"Goyal Palash","year":"2020","unstructured":"Palash Goyal, Sujit Rokka Chhetri, and Arquimedes Canedo. 2020. dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowledge-Based Systems 187 (2020), 104816.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_2_91_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","article-title":"Graph embedding techniques, applications, and performance: A survey","volume":"151","author":"Goyal Palash","year":"2018","unstructured":"Palash Goyal and Emilio Ferrara. 2018. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems 151 (2018), 78\u201394.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_2_92_2","article-title":"DynGEM: Deep embedding method for dynamic graphs","author":"Goyal Palash","year":"2017","unstructured":"Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2017. DynGEM: Deep embedding method for dynamic graphs. 3rd International Workshop on Representation Learning for Graphs (ReLiG), IJCAI 2017 (2017).","journal-title":"3rd International Workshop on Representation Learning for Graphs (ReLiG), IJCAI 2017"},{"issue":"4","key":"e_1_3_2_93_2","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1137\/120880173","article-title":"Geometrical structure of Laplacian eigenfunctions","volume":"55","author":"Grebenkov Denis S.","year":"2013","unstructured":"Denis S. Grebenkov and B.-T. Nguyen. 2013. Geometrical structure of Laplacian eigenfunctions. SIAM Review 55, 4 (2013), 601\u2013667.","journal-title":"SIAM Review"},{"key":"e_1_3_2_94_2","first-page":"855","volume-title":"Proceedings of the 22nd ACM SIGKDD","author":"Grover Aditya","year":"2016","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD. ACM, 855\u2013864."},{"key":"e_1_3_2_95_2","first-page":"11984","article-title":"Implicit graph neural networks","volume":"33","author":"Gu Fangda","year":"2020","unstructured":"Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, and Laurent El Ghaoui. 2020. Implicit graph neural networks. Advances in Neural Information Processing Systems 33 (2020), 11984\u201311995.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_96_2","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1145\/3459637.3482490","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Guan Renchu","year":"2021","unstructured":"Renchu Guan, Yonghao Liu, Xiaoyue Feng, and Ximing Li. 2021. VPALG: Paper-publication prediction with graph neural networks. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 617\u2013626."},{"issue":"1","key":"e_1_3_2_97_2","first-page":"1","article-title":"Hierarchical hyperedge embedding-based representation learning for group recommendation","volume":"40","author":"Guo Lei","year":"2021","unstructured":"Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. 2021. Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Transactions on Information Systems (TOIS) 40, 1 (2021), 1\u201327.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_98_2","first-page":"922","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Guo Shengnan","year":"2019","unstructured":"Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922\u2013929."},{"key":"e_1_3_2_99_2","article-title":"Variational graph recurrent neural networks","volume":"32","author":"Hajiramezanali Ehsan","year":"2019","unstructured":"Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Variational graph recurrent neural networks. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_100_2","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"e_1_3_2_101_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-01588-5","article-title":"Graph representation learning","volume":"14","author":"Hamilton William L.","year":"2020","unstructured":"William L. Hamilton. 2020. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 14, 3 (2020), 1\u2013159.","journal-title":"Synthesis Lectures on Artificial Intelligence and Machine Learning"},{"key":"e_1_3_2_102_2","article-title":"Representation learning on graphs: Methods and applications","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).","journal-title":"arXiv preprint arXiv:1709.05584"},{"key":"e_1_3_2_103_2","first-page":"547","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Han Liangzhe","year":"2021","unstructured":"Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. 2021. Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 547\u2013555."},{"key":"e_1_3_2_104_2","first-page":"4094","volume-title":"International Conference on Machine Learning","author":"Hasanzadeh Arman","year":"2020","unstructured":"Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, and Xiaoning Qian. 2020. Bayesian graph neural networks with adaptive connection sampling. In International Conference on Machine Learning. PMLR, 4094\u20134104."},{"key":"e_1_3_2_105_2","article-title":"ColdGuess: A general and effective relational graph convolutional network to tackle cold start cases","author":"He Bo","year":"2022","unstructured":"Bo He, Xiang Song, Vincent Gao, and Christos Faloutsos. 2022. ColdGuess: A general and effective relational graph convolutional network to tackle cold start cases. arXiv preprint arXiv:2205.12318 (2022).","journal-title":"arXiv preprint arXiv:2205.12318"},{"key":"e_1_3_2_106_2","first-page":"1466","volume-title":"Proceedings of the ACM Web Conference 2022","author":"He Dongxiao","year":"2022","unstructured":"Dongxiao He, Rui Guo, Xiaobao Wang, Di Jin, Yuxiao Huang, and Wenjun Wang. 2022. Inflation improves graph neural networks. In Proceedings of the ACM Web Conference 2022. 1466\u20131474."},{"key":"e_1_3_2_107_2","first-page":"4091","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"He Tao","year":"2020","unstructured":"Tao He, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, and Yuanfang Li. 2020. SNEQ: Semi-supervised attributed network embedding with attention-based quantisation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 4091\u20134098."},{"key":"e_1_3_2_108_2","first-page":"2641","article-title":"Learning conjoint attentions for graph neural nets","volume":"34","author":"He Tiantian","year":"2021","unstructured":"Tiantian He, Yew Soon Ong, and Lu Bai. 2021. Learning conjoint attentions for graph neural nets. Advances in Neural Information Processing Systems 34 (2021), 2641\u20132653.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_109_2","first-page":"355","volume-title":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 355\u2013364."},{"issue":"1","key":"e_1_3_2_110_2","first-page":"1","article-title":"Evolving network representation learning based on random walks","volume":"5","author":"Heidari Farzaneh","year":"2020","unstructured":"Farzaneh Heidari and Manos Papagelis. 2020. Evolving network representation learning based on random walks. Applied Network Science 5, 1 (2020), 1\u201338.","journal-title":"Applied Network Science"},{"key":"e_1_3_2_111_2","first-page":"9","article-title":"Distilling the knowledge in a neural network","volume":"1050","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. stat 1050 (2015), 9.","journal-title":"stat"},{"key":"e_1_3_2_112_2","first-page":"594","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hou Zhenyu","year":"2022","unstructured":"Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022. GraphMAE: Self-supervised masked graph autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 594\u2013604."},{"key":"e_1_3_2_113_2","first-page":"1857","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Hu Ziniu","year":"2020","unstructured":"Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun. 2020. GPT-GNN: Generative pre-training of graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1857\u20131867."},{"key":"e_1_3_2_114_2","first-page":"2704","volume-title":"Proceedings of the Web Conference 2020","author":"Hu Ziniu","year":"2020","unstructured":"Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020. Heterogeneous graph transformer. In Proceedings of the Web Conference 2020. 2704\u20132710."},{"key":"e_1_3_2_115_2","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/ICDE51399.2021.00054","volume-title":"2021 IEEE 37th International Conference on Data Engineering (ICDE)","author":"Huan Zhao","year":"2021","unstructured":"Zhao Huan, Yao Quanming, and Tu Weiwei. 2021. Search to aggregate neighborhood for graph neural network. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 552\u2013563."},{"key":"e_1_3_2_116_2","first-page":"4123","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Huang Chao","year":"2021","unstructured":"Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, and Jimmy Xiangji Huang. 2021. Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4123\u20134130."},{"key":"e_1_3_2_117_2","first-page":"4115","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Huang Chao","year":"2021","unstructured":"Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, and Yanfang Ye. 2021. Knowledge-aware coupled graph neural network for social recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4115\u20134122."},{"key":"e_1_3_2_118_2","first-page":"1237","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-20)","author":"Huang Hong","unstructured":"Hong Huang, Zixuan Fang, Xiao Wang, Youshan Miao, and Hai Jin. [n.d.]. Motif-preserving temporal network embedding. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-20). 1237\u20131243."},{"key":"e_1_3_2_119_2","doi-asserted-by":"crossref","first-page":"3032","DOI":"10.1145\/3447548.3467059","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Huang Jizhou","year":"2021","unstructured":"Jizhou Huang, Haifeng Wang, Yibo Sun, Miao Fan, Zhengjie Huang, Chunyuan Yuan, and Yawen Li. 2021. HGAMN: Heterogeneous graph attention matching network for multilingual POI retrieval at Baidu maps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3032\u20133040."},{"key":"e_1_3_2_120_2","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1109\/ICDE48307.2020.00101","volume-title":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","author":"Huang Shixun","year":"2020","unstructured":"Shixun Huang, Zhifeng Bao, Guoliang Li, Yanghao Zhou, and J. Shane Culpepper. 2020. Temporal network representation learning via historical neighborhoods aggregation. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1117\u20131128."},{"key":"e_1_3_2_121_2","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/DSAA49011.2020.00047","volume-title":"2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)","author":"Huang Tong","year":"2020","unstructured":"Tong Huang, Lihua Zhou, Lizhen Wang, Guowang Du, and Kevin L\u00fc. 2020. Attributed network embedding with community preservation. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 334\u2013343."},{"key":"e_1_3_2_122_2","article-title":"Adaptive sampling towards fast graph representation learning","volume":"31","author":"Huang Wenbing","year":"2018","unstructured":"Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018. Adaptive sampling towards fast graph representation learning. Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_123_2","first-page":"633","volume-title":"Proceedings of the 2017 SIAM International Conference on Data Mining","author":"Huang Xiao","year":"2017","unstructured":"Xiao Huang, Jundong Li, and Xia Hu. 2017. Accelerated attributed network embedding. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 633\u2013641."},{"key":"e_1_3_2_124_2","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1145\/3018661.3018667","volume-title":"Proceedings of the 10th ACM International Conference on Web Search and Data Mining","author":"Huang Xiao","year":"2017","unstructured":"Xiao Huang, Jundong Li, and Xia Hu. 2017. Label informed attributed network embedding. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 731\u2013739."},{"key":"e_1_3_2_125_2","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1145\/3447548.3467300","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Huang Zexi","year":"2021","unstructured":"Zexi Huang, Arlei Silva, and Ambuj Singh. 2021. A broader picture of random-walk based graph embedding. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 685\u2013695."},{"key":"e_1_3_2_126_2","article-title":"Going deeper into permutation-sensitive graph neural networks","author":"Huang Zhongyu","year":"2022","unstructured":"Zhongyu Huang, Yingheng Wang, Chaozhuo Li, and Huiguang He. 2022. Going deeper into permutation-sensitive graph neural networks. ICML (2022).","journal-title":"ICML"},{"key":"e_1_3_2_127_2","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1145\/3447548.3467256","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Huang Zengfeng","year":"2021","unstructured":"Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, and Min Zhou. 2021. Scaling up graph neural networks via graph coarsening. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 675\u2013684."},{"key":"e_1_3_2_128_2","first-page":"2186","volume-title":"International Conference on Machine Learning","author":"Ivanov Sergey","year":"2018","unstructured":"Sergey Ivanov and Evgeny Burnaev. 2018. Anonymous walk embeddings. In International Conference on Machine Learning. PMLR, 2186\u20132195."},{"key":"e_1_3_2_129_2","first-page":"997","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Jia Zhihao","year":"2020","unstructured":"Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken. 2020. Redundancy-free computation for graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 997\u20131005."},{"key":"e_1_3_2_130_2","first-page":"1324","volume-title":"IJCAI","author":"Jia Ziyu","year":"2020","unstructured":"Ziyu Jia, Youfang Lin, Jing Wang, Ronghao Zhou, Xiaojun Ning, Yuanlai He, and Yaoshuai Zhao. 2020. GraphSleepNet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In IJCAI. 1324\u20131330."},{"key":"e_1_3_2_131_2","first-page":"324","volume-title":"Proceedings of the 2018 SIAM International Conference on Data Mining","author":"Jiang Fei","year":"2018","unstructured":"Fei Jiang, Lifang He, Yi Zheng, Enqiang Zhu, Jin Xu, and Philip S. Yu. 2018. On spectral graph embedding: A non-backtracking perspective and graph approximation. In Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM, 324\u2013332."},{"key":"e_1_3_2_132_2","first-page":"2635","volume-title":"IJCAI","author":"Jiang Jianwen","year":"2019","unstructured":"Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, and Yue Gao. 2019. Dynamic hypergraph neural networks. In IJCAI. 2635\u20132641."},{"key":"e_1_3_2_133_2","first-page":"659","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Jin Bowen","year":"2020","unstructured":"Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2020. Multi-behavior recommendation with graph convolutional networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 659\u2013668."},{"key":"e_1_3_2_134_2","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1145\/3442381.3449914","volume-title":"Proceedings of the Web Conference 2021","author":"Jin Di","year":"2021","unstructured":"Di Jin, Cuiying Huo, Chundong Liang, and Liang Yang. 2021. Heterogeneous graph neural network via attribute completion. In Proceedings of the Web Conference 2021. 391\u2013400."},{"key":"e_1_3_2_135_2","article-title":"Universal graph convolutional networks","volume":"34","author":"Jin Di","year":"2021","unstructured":"Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, and Jiawei Han. 2021. Universal graph convolutional networks. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_136_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/BF02289026","article-title":"A new status index derived from sociometric analysis","volume":"18","author":"Katz Leo","year":"1953","unstructured":"Leo Katz. 1953. A new status index derived from sociometric analysis. Psychometrika 18, 1 (1953), 39\u201343.","journal-title":"Psychometrika"},{"issue":"70","key":"e_1_3_2_137_2","first-page":"1","article-title":"Representation learning for dynamic graphs: A survey.","volume":"21","author":"Kazemi Seyed Mehran","year":"2020","unstructured":"Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart. 2020. Representation learning for dynamic graphs: A survey. Journal of Machine Learning Research 21, 70 (2020), 1\u201373.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_138_2","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/BigData55660.2022.10020299","volume-title":"2022 IEEE International Conference on Big Data (Big Data)","author":"Khoshraftar Shima","year":"2022","unstructured":"Shima Khoshraftar, Aijun An, and Nastaran Babanejad. 2022. Temporal graph representation learning via maximal cliques. In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 606\u2013615."},{"key":"e_1_3_2_139_2","first-page":"1","volume-title":"2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)","author":"Khoshraftar Shima","year":"2021","unstructured":"Shima Khoshraftar, Sedigheh Mahdavi, and Aijun An. 2021. Centrality-based interpretability measures for graph embeddings. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 1\u201310."},{"key":"e_1_3_2_140_2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/DSAA.2019.00026","volume-title":"2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","author":"Khoshraftar Shima","year":"2019","unstructured":"Shima Khoshraftar, Sedigheh Mahdavi, Aijun An, Yonggang Hu, and Junfeng Liu. 2019. Dynamic graph embedding via LSTM history tracking. In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 119\u2013127."},{"key":"e_1_3_2_141_2","first-page":"1","article-title":"Auto-encoding variational Bayes","volume":"1050","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational Bayes. stat 1050 (2014), 1.","journal-title":"stat"},{"key":"e_1_3_2_142_2","article-title":"Variational graph auto-encoders","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).","journal-title":"arXiv preprint arXiv:1611.07308"},{"key":"e_1_3_2_143_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR (2017).","journal-title":"ICLR"},{"key":"e_1_3_2_144_2","volume-title":"International Conference on Learning Representations","author":"Klicpera Johannes","year":"2018","unstructured":"Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized PageRank. In International Conference on Learning Representations."},{"key":"e_1_3_2_145_2","first-page":"13366","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera, Stefan Wei\u00dfenberger, and Stephan G\u00fcnnemann. 2019. Diffusion improves graph learning. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 13366\u201313378."},{"key":"e_1_3_2_146_2","first-page":"2341","volume-title":"IJCAI","author":"Kong Dejiang","year":"2018","unstructured":"Dejiang Kong and Fei Wu. 2018. HST-LSTM: A hierarchical spatial-temporal long-short term memory network for location prediction. In IJCAI, Vol. 18. 2341\u20132347."},{"key":"e_1_3_2_147_2","first-page":"461","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Lai Kwei-Herng","year":"2020","unstructured":"Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, and Xia Hu. 2020. Policy-GNN: Aggregation optimization for graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 461\u2013471."},{"key":"e_1_3_2_148_2","article-title":"PRUNE: Preserving proximity and global ranking for network embedding","volume":"30","author":"Lai Yi-An","year":"2017","unstructured":"Yi-An Lai, Chin-Chi Hsu, Wen Hao Chen, Mi-Yen Yeh, and Shou-De Lin. 2017. PRUNE: Preserving proximity and global ranking for network embedding. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_149_2","first-page":"263","volume-title":"Proceedings of the 3rd International Scientific Conference \u201cIntelligent Information Technologies for Industry\u201d (IITI\u201918) Volume 13","author":"Le Vang","year":"2019","unstructured":"Vang Le and Vaclav Snasel. 2019. Community detection in online social network using graph embedding and hierarchical clustering. In Proceedings of the 3rd International Scientific Conference \u201cIntelligent Information Technologies for Industry\u201d (IITI\u201918) Volume 13. Springer, 263\u2013272."},{"key":"e_1_3_2_150_2","first-page":"499","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Lee John Boaz","year":"2019","unstructured":"John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao. 2019. Graph convolutional networks with motif-based attention. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 499\u2013508."},{"issue":"9","key":"e_1_3_2_151_2","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"Leman A. A.","year":"1968","unstructured":"A. A. Leman and B. Weisfeiler. 1968. A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsiya 2, 9 (1968), 12\u201316.","journal-title":"Nauchno-Technicheskaya Informatsiya"},{"issue":"21","key":"e_1_3_2_152_2","doi-asserted-by":"crossref","first-page":"16647","DOI":"10.1007\/s00521-020-04908-5","article-title":"Network representation learning: A systematic literature review","volume":"32","author":"Li Bentian","year":"2020","unstructured":"Bentian Li and Dechang Pi. 2020. Network representation learning: A systematic literature review. Neural Computing and Applications 32, 21 (2020), 16647\u201316679.","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_2_153_2","first-page":"6437","volume-title":"International Conference on Machine Learning","author":"Li Guohao","year":"2021","unstructured":"Guohao Li, Matthias M\u00fcller, Bernard Ghanem, and Vladlen Koltun. 2021. Training graph neural networks with 1000 layers. In International Conference on Machine Learning. PMLR, 6437\u20136449."},{"key":"e_1_3_2_154_2","first-page":"387","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","author":"Li Jundong","year":"2017","unstructured":"Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. 2017. Attributed network embedding for learning in a dynamic environment. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 387\u2013396."},{"issue":"2","key":"e_1_3_2_155_2","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TCSS.2021.3050397","article-title":"A community detection method for social network based on community embedding","volume":"8","author":"Li Meizi","year":"2021","unstructured":"Meizi Li, Shuyi Lu, Lele Zhang, Yuping Zhang, and Bo Zhang. 2021. A community detection method for social network based on community embedding. IEEE Transactions on Computational Social Systems 8, 2 (2021), 308\u2013318.","journal-title":"IEEE Transactions on Computational Social Systems"},{"issue":"41","key":"e_1_3_2_156_2","doi-asserted-by":"crossref","first-page":"27233","DOI":"10.1021\/acsomega.1c04017","article-title":"DGL-LifeSci: An open-source toolkit for deep learning on graphs in life science","volume":"6","author":"Li Mufei","year":"2021","unstructured":"Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, and George Karypis. 2021. DGL-LifeSci: An open-source toolkit for deep learning on graphs in life science. ACS Omega 6, 41 (2021), 27233\u201327238.","journal-title":"ACS Omega"},{"key":"e_1_3_2_157_2","first-page":"4189","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Li Mengzhang","year":"2021","unstructured":"Mengzhang Li and Zhanxing Zhu. 2021. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4189\u20134196."},{"key":"e_1_3_2_158_2","first-page":"4465","article-title":"Distance encoding: Design provably more powerful neural networks for graph representation learning","volume":"33","author":"Li Pan","year":"2020","unstructured":"Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. 2020. Distance encoding: Design provably more powerful neural networks for graph representation learning. Advances in Neural Information Processing Systems 33 (2020), 4465\u20134478.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_159_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Li Qimai","year":"2018","unstructured":"Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_2_160_2","first-page":"953","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Li Qimai","year":"2021","unstructured":"Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai, and Xiao-Ming Wu. 2021. Dimensionwise separable 2-D graph convolution for unsupervised and semi-supervised learning on graphs. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 953\u2013963."},{"key":"e_1_3_2_161_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Li Ruoyu","year":"2018","unstructured":"Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018. Adaptive graph convolutional neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_2_162_2","article-title":"Finding global homophily in graph neural networks when meeting heterophily","author":"Li Xiang","year":"2022","unstructured":"Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. 2022. Finding global homophily in graph neural networks when meeting heterophily. ICML (2022).","journal-title":"ICML"},{"key":"e_1_3_2_163_2","first-page":"1209","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"Li Yinfeng","year":"2022","unstructured":"Yinfeng Li, Chen Gao, Xiaoyi Du, Huazhou Wei, Hengliang Luo, Depeng Jin, and Yong Li. 2022. Spatiotemporal-aware session-based recommendation with graph neural networks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1209\u20131218."},{"key":"e_1_3_2_164_2","volume-title":"32nd AAAI Conference on Artificial Intelligence","author":"Li Ye","year":"2018","unstructured":"Ye Li, Chaofeng Sha, Xin Huang, and Yanchun Zhang. 2018. Community detection in attributed graphs: An embedding approach. In 32nd AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_165_2","volume-title":"International Conference on Learning Representations","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations."},{"key":"e_1_3_2_166_2","volume-title":"Proceedings of ICLR\u201916","author":"Li Yujia","year":"2016","unstructured":"Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow. 2016. Gated graph sequence neural networks. In Proceedings of ICLR\u201916."},{"key":"e_1_3_2_167_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1137\/1.9781611975673.12","volume-title":"Proceedings of the 2019 SIAM International Conference on Data Mining","author":"Liang Xiaoyuan","year":"2019","unstructured":"Xiaoyuan Liang, Guiling Wang, Martin Renqiang Min, Yi Qi, and Zhu Han. 2019. A deep spatio-temporal fuzzy neural network for passenger demand prediction. In Proceedings of the 2019 SIAM International Conference on Data Mining. SIAM, 100\u2013108."},{"issue":"7","key":"e_1_3_2_168_2","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1002\/asi.20591","article-title":"The link-prediction problem for social networks","volume":"58","author":"Liben-Nowell David","year":"2007","unstructured":"David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58, 7 (2007), 1019\u20131031.","journal-title":"Journal of the American Society for Information Science and Technology"},{"key":"e_1_3_2_169_2","first-page":"845","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Lim Nicholas","year":"2020","unstructured":"Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, and Jagannadan Varadarajan. 2020. STP-UDGAT: Spatial-temporal-preference user dimensional graph attention network for next POI recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 845\u2013854."},{"key":"e_1_3_2_170_2","first-page":"36","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Lin Haoxing","year":"2020","unstructured":"Haoxing Lin, Rufan Bai, Weijia Jia, Xinyu Yang, and Yongjian You. 2020. Preserving dynamic attention for long-term spatial-temporal prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 36\u201346."},{"key":"e_1_3_2_171_2","first-page":"7083","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Lin Ji","year":"2019","unstructured":"Ji Lin, Chuang Gan, and Song Han. 2019. TSM: Temporal shift module for efficient video understanding. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 7083\u20137093."},{"key":"e_1_3_2_172_2","first-page":"11531","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Lin Zhihui","year":"2020","unstructured":"Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, and Chun Yuan. 2020. Self-attention ConvLSTM for spatiotemporal prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 11531\u201311538."},{"key":"e_1_3_2_173_2","first-page":"1228","volume-title":"2019 IEEE International Conference on Data Mining (ICDM)","author":"Liu Hongbin","year":"2019","unstructured":"Hongbin Liu, Hao Wu, Weiwei Sun, and Ickjai Lee. 2019. Spatio-temporal GRU for trajectory classification. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1228\u20131233."},{"key":"e_1_3_2_174_2","first-page":"8653","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Liu Huihui","year":"2021","unstructured":"Huihui Liu, Yiding Yang, and Xinchao Wang. 2021. Overcoming catastrophic forgetting in graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 8653\u20138661."},{"key":"e_1_3_2_175_2","article-title":"EIGNN: Efficient infinite-depth graph neural networks","volume":"34","author":"Liu Juncheng","year":"2021","unstructured":"Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, and Xiaokui Xiao. 2021. EIGNN: Efficient infinite-depth graph neural networks. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_176_2","first-page":"338","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Liu Meng","year":"2020","unstructured":"Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020. Towards deeper graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 338\u2013348."},{"key":"e_1_3_2_177_2","first-page":"2202","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Liu Meng","year":"2021","unstructured":"Meng Liu and Yong Liu. 2021. Inductive representation learning in temporal networks via mining neighborhood and community influences. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2202\u20132206."},{"key":"e_1_3_2_178_2","article-title":"Non-local graph neural networks","author":"Liu Meng","year":"2021","unstructured":"Meng Liu, Zhengyang Wang, and Shuiwang Ji. 2021. Non-local graph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_179_2","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1145\/3340531.3416021","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Liu Qi","year":"2020","unstructured":"Qi Liu, Ruobing Xie, Lei Chen, Shukai Liu, Ke Tu, Peng Cui, Bo Zhang, and Leyu Lin. 2020. Graph neural network for tag ranking in tag-enhanced video recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2613\u20132620."},{"key":"e_1_3_2_180_2","first-page":"6837","volume-title":"International Conference on Machine Learning","author":"Liu Xiaorui","year":"2021","unstructured":"Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang. 2021. Elastic graph neural networks. In International Conference on Machine Learning. PMLR, 6837\u20136849."},{"key":"e_1_3_2_181_2","first-page":"375","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining","author":"Liu Xin","year":"2019","unstructured":"Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, and Chenyi Zhuang. 2019. A general view for network embedding as matrix factorization. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 375\u2013383."},{"key":"e_1_3_2_182_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.aiopen.2021.02.001","article-title":"Network representation learning: A macro and micro view","volume":"2","author":"Liu Xueyi","year":"2021","unstructured":"Xueyi Liu and Jie Tang. 2021. Network representation learning: A macro and micro view. AI Open 2 (2021), 43\u201364.","journal-title":"AI Open"},{"key":"e_1_3_2_183_2","first-page":"2621","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Liu Yiding","year":"2020","unstructured":"Yiding Liu, Yulong Gu, Zhuoye Ding, Junchao Gao, Ziyi Guo, Yongjun Bao, and Weipeng Yan. 2020. Decoupled graph convolution network for inferring substitutable and complementary items. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2621\u20132628."},{"key":"e_1_3_2_184_2","article-title":"Graph self-supervised learning: A survey","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip Yu. 2022. Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering (2022).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_185_2","first-page":"1629","volume-title":"Proceedings of the Web Conference 2021","author":"Liu Zhijun","year":"2021","unstructured":"Zhijun Liu, Chao Huang, Yanwei Yu, and Junyu Dong. 2021. Motif-preserving dynamic attributed network embedding. In Proceedings of the Web Conference 2021. 1629\u20131638."},{"key":"e_1_3_2_186_2","first-page":"1005","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Liu Zhijun","year":"2020","unstructured":"Zhijun Liu, Chao Huang, Yanwei Yu, Peng Song, Baode Fan, and Junyu Dong. 2020. Dynamic representation learning for large-scale attributed networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1005\u20131014."},{"key":"e_1_3_2_187_2","first-page":"3976","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Liu Zongtao","year":"2021","unstructured":"Zongtao Liu, Bin Ma, Quan Liu, Jian Xu, and Bo Zheng. 2021. Heterogeneous graph neural networks for large-scale bid keyword matching. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3976\u20133985."},{"key":"e_1_3_2_188_2","first-page":"1109","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Liu Zemin","year":"2021","unstructured":"Zemin Liu, Trung-Kien Nguyen, and Yuan Fang. 2021. Tail-GNN: Tail-node graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1109\u20131119."},{"key":"e_1_3_2_189_2","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1145\/3394486.3403150","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Long Qingqing","year":"2020","unstructured":"Qingqing Long, Yilun Jin, Guojie Song, Yi Li, and Wei Lin. 2020. Graph structural-topic neural network. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1065\u20131073."},{"key":"e_1_3_2_190_2","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1145\/3442381.3449951","volume-title":"Proceedings of the Web Conference 2021","author":"Long Qingqing","year":"2021","unstructured":"Qingqing Long, Yilun Jin, Yi Wu, and Guojie Song. 2021. Theoretically improving graph neural networks via anonymous walk graph kernels. In Proceedings of the Web Conference 2021. 1204\u20131214."},{"key":"e_1_3_2_191_2","first-page":"1025","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Lu Bin","year":"2020","unstructured":"Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, and Haisong Zhang. 2020. Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1025\u20131034."},{"key":"e_1_3_2_192_2","first-page":"469","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Lu Yuanfu","year":"2019","unstructured":"Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, and Yanfang Ye. 2019. Temporal network embedding with micro- and macro-dynamics. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 469\u2013478."},{"key":"e_1_3_2_193_2","first-page":"7180","volume-title":"International Conference on Machine Learning","author":"Lukovnikov Denis","year":"2021","unstructured":"Denis Lukovnikov and Asja Fischer. 2021. Improving breadth-wise backpropagation in graph neural networks helps learning long-range dependencies. In International Conference on Machine Learning. PMLR, 7180\u20137191."},{"key":"e_1_3_2_194_2","first-page":"3213","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Luo Wenjuan","year":"2020","unstructured":"Wenjuan Luo, Han Zhang, Xiaodi Yang, Lin Bo, Xiaoqing Yang, Zang Li, Xiaohu Qie, and Jieping Ye. 2020. Dynamic heterogeneous graph neural network for real-time event prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3213\u20133223."},{"key":"e_1_3_2_195_2","first-page":"2177","volume-title":"Proceedings of the Web Conference 2021","author":"Luo Yingtao","year":"2021","unstructured":"Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-temporal attention network for next location recommendation. In Proceedings of the Web Conference 2021. 2177\u20132185."},{"key":"e_1_3_2_196_2","article-title":"Clique pooling for graph classification","author":"Luzhnica Enxhell","year":"2019","unstructured":"Enxhell Luzhnica, Ben Day, and Pietro Lio. 2019. Clique pooling for graph classification. arXiv preprint arXiv:1904.00374 (2019).","journal-title":"arXiv preprint arXiv:1904.00374"},{"key":"e_1_3_2_197_2","first-page":"1150","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Lv Qingsong","year":"2021","unstructured":"Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. 2021. Are we really making much progress? Revisiting, benchmarking and refining heterogeneous graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1150\u20131160."},{"key":"e_1_3_2_198_2","first-page":"147","volume-title":"Proceedings of the 2017 ACM Conference on Information and Knowledge Management","author":"Lyu Tianshu","year":"2017","unstructured":"Tianshu Lyu, Yuan Zhang, and Yan Zhang. 2017. Enhancing the network embedding quality with structural similarity. In Proceedings of the 2017 ACM Conference on Information and Knowledge Management. 147\u2013156."},{"key":"e_1_3_2_199_2","first-page":"3313","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Ma Jing","year":"2021","unstructured":"Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, and Joyce C. Ho. 2021. Temporal network embedding via tensor factorization. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3313\u20133317."},{"key":"e_1_3_2_200_2","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1145\/3397271.3401092","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Ma Yao","year":"2020","unstructured":"Yao Ma, Ziyi Guo, Zhaocun Ren, Jiliang Tang, and Dawei Yin. 2020. Streaming graph neural networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 719\u2013728."},{"key":"e_1_3_2_201_2","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1145\/3459637.3482225","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Ma Yao","year":"2021","unstructured":"Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah. 2021. A unified view on graph neural networks as graph signal denoising. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1202\u20131211."},{"key":"e_1_3_2_202_2","doi-asserted-by":"crossref","DOI":"10.1017\/9781108924184","volume-title":"Deep Learning on Graphs","author":"Ma Yao","year":"2021","unstructured":"Yao Ma and Jiliang Tang. 2021. Deep Learning on Graphs. Cambridge University Press."},{"key":"e_1_3_2_203_2","first-page":"16421","article-title":"Path integral based convolution and pooling for graph neural networks","volume":"33","author":"Ma Zheng","year":"2020","unstructured":"Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, and Pietro Li\u00f2. 2020. Path integral based convolution and pooling for graph neural networks. Advances in Neural Information Processing Systems 33 (2020), 16421\u201316433.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_204_2","first-page":"281","volume-title":"Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability","volume":"1","author":"MacQueen James","year":"1967","unstructured":"James MacQueen et\u00a0al. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1. Oakland, CA, USA, 281\u2013297."},{"key":"e_1_3_2_205_2","doi-asserted-by":"crossref","first-page":"3762","DOI":"10.1109\/BigData.2018.8621910","volume-title":"2018 IEEE International Conference on Big Data (Big Data)","author":"Mahdavi Sedigheh","year":"2018","unstructured":"Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. 2018. dynnode2vec: Scalable dynamic network embedding. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 3762\u20133765."},{"key":"e_1_3_2_206_2","first-page":"385","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"Mahdavi Sedigheh","year":"2019","unstructured":"Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. 2019. Dynamic joint variational graph autoencoders. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 385\u2013401."},{"key":"e_1_3_2_207_2","article-title":"Provably powerful graph networks","volume":"32","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. Provably powerful graph networks. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_208_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","article-title":"Birds of a feather: Homophily in social networks","volume":"27","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson, Lynn Smith-Lovin, and James M. Cook. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 1 (2001), 415\u2013444.","journal-title":"Annual Review of Sociology"},{"key":"e_1_3_2_209_2","first-page":"393","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining","author":"Meng Zaiqiao","year":"2019","unstructured":"Zaiqiao Meng, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. 2019. Co-embedding attributed networks. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 393\u2013401."},{"key":"e_1_3_2_210_2","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1145\/3437963.3441785","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Miao Congcong","year":"2021","unstructured":"Congcong Miao, Jiajun Fu, Jilong Wang, Heng Yu, Botao Yao, Anqi Zhong, Jie Chen, and Zekun He. 2021. Predicting crowd flows via pyramid dilated deeper spatial-temporal network. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 806\u2013814."},{"key":"e_1_3_2_211_2","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1145\/3447548.3467312","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Miao Xupeng","year":"2021","unstructured":"Xupeng Miao, Nezihe Merve G\u00fcrel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Susie Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, et\u00a0al. 2021. DeGNN: Improving graph neural networks with graph decomposition. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1223\u20131233."},{"key":"e_1_3_2_212_2","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).","journal-title":"arXiv preprint arXiv:1301.3781"},{"key":"e_1_3_2_213_2","first-page":"4602","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Morris Christopher","year":"2019","unstructured":"Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and Leman go neural: Higher-order graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4602\u20134609."},{"key":"e_1_3_2_214_2","first-page":"4663","volume-title":"International Conference on Machine Learning","author":"Murphy Ryan","year":"2019","unstructured":"Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro. 2019. Relational pooling for graph representations. In International Conference on Machine Learning. PMLR, 4663\u20134673."},{"key":"e_1_3_2_215_2","unstructured":"Vidit Nanda. 2021. Computational algebraic topology lecture notes. https:\/\/people.maths.ox.ac.uk\/nanda\/cat\/TDANotes"},{"key":"e_1_3_2_216_2","first-page":"969","volume-title":"Companion Proceedings of the The Web Conference 2018","author":"Nguyen Giang Hoang","year":"2018","unstructured":"Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, and Sungchul Kim. 2018. Continuous-time dynamic network embeddings. In Companion Proceedings of the The Web Conference 2018. 969\u2013976."},{"key":"e_1_3_2_217_2","volume-title":"31st AAAI Conference on Artificial Intelligence","author":"Nie Feiping","year":"2017","unstructured":"Feiping Nie, Wei Zhu, and Xuelong Li. 2017. Unsupervised large graph embedding. In 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_218_2","first-page":"2014","volume-title":"International Conference on Machine Learning","author":"Niepert Mathias","year":"2016","unstructured":"Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In International Conference on Machine Learning. PMLR, 2014\u20132023."},{"key":"e_1_3_2_219_2","first-page":"16211","article-title":"Random walk graph neural networks","volume":"33","author":"Nikolentzos Giannis","year":"2020","unstructured":"Giannis Nikolentzos and Michalis Vazirgiannis. 2020. Random walk graph neural networks. Advances in Neural Information Processing Systems 33 (2020), 16211\u201316222.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_220_2","first-page":"3405","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Niu Xichuan","year":"2020","unstructured":"Xichuan Niu, Bofang Li, Chenliang Li, Rong Xiao, Haochuan Sun, Hongbo Deng, and Zhenzhong Chen. 2020. A dual heterogeneous graph attention network to improve long-tail performance for shop search in e-commerce. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3405\u20133415."},{"key":"e_1_3_2_221_2","volume-title":"International Conference on Learning Representations","author":"Oono Kenta","year":"2019","unstructured":"Kenta Oono and Taiji Suzuki. 2019. Graph neural networks exponentially lose expressive power for node classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_222_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Oreshkin Boris N.","year":"2021","unstructured":"Boris N. Oreshkin, Arezou Amini, Lucy Coyle, and Mark J. Coates. 2021. FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_223_2","first-page":"1105","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Ou Mingdong","year":"2016","unstructured":"Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1105\u20131114."},{"key":"e_1_3_2_224_2","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1145\/3292500.3330655","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Ouyang Wentao","year":"2019","unstructured":"Wentao Ouyang, Xiuwu Zhang, Li Li, Heng Zou, Xin Xing, Zhaojie Liu, and Yanlong Du. 2019. Deep spatio-temporal neural networks for click-through rate prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2078\u20132086."},{"key":"e_1_3_2_225_2","first-page":"797","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Pan Guosheng","year":"2021","unstructured":"Guosheng Pan, Yuan Yao, Hanghang Tong, Feng Xu, and Jian Lu. 2021. Unsupervised attributed network embedding via cross fusion. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 797\u2013805."},{"key":"e_1_3_2_226_2","article-title":"DropGNN: Random dropouts increase the expressiveness of graph neural networks","volume":"34","author":"Papp P\u00e1l Andr\u00e1s","year":"2021","unstructured":"P\u00e1l Andr\u00e1s Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer. 2021. DropGNN: Random dropouts increase the expressiveness of graph neural networks. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_227_2","first-page":"5363","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Pareja Aldo","year":"2020","unstructured":"Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. 2020. EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5363\u20135370."},{"key":"e_1_3_2_228_2","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1145\/3340531.3411940","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Park Cheonbok","year":"2020","unstructured":"Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Seungmin Jin, Kihwan Kim, Sungahn Ko, and Jaegul Choo. 2020. ST-GRAT: A novel spatio-temporal graph attention network for accurately forecasting dynamically changing road speed. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1215\u20131224."},{"key":"e_1_3_2_229_2","volume-title":"International Conference on Learning Representations","author":"Pei Hongbin","year":"2019","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2019. Geom-GCN: Geometric graph convolutional networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_230_2","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/ICDM51629.2021.00059","volume-title":"2021 IEEE International Conference on Data Mining (ICDM)","author":"Peng Jingshu","year":"2021","unstructured":"Jingshu Peng, Yanyan Shen, and Lei Chen. 2021. GraphANGEL: Adaptive aNd structure-aware sampling on graph NEuraL networks. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 479\u2013488."},{"key":"e_1_3_2_231_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1145\/3366423.3380112","volume-title":"Proceedings of The Web Conference 2020","author":"Peng Zhen","year":"2020","unstructured":"Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. 2020. Graph representation learning via graphical mutual information maximization. In Proceedings of The Web Conference 2020. 259\u2013270."},{"key":"e_1_3_2_232_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Perez Ethan","year":"2018","unstructured":"Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018. Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_2_233_2","first-page":"701","volume-title":"Proceedings of the 20th ACM SIGKDD","author":"Perozzi Bryan","year":"2014","unstructured":"Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD. ACM, 701\u2013710."},{"key":"e_1_3_2_234_2","first-page":"1509","volume-title":"The World Wide Web Conference","author":"Qiu Jiezhong","year":"2019","unstructured":"Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. 2019. NetSMF: Large-scale network embedding as sparse matrix factorization. In The World Wide Web Conference. 1509\u20131520."},{"key":"e_1_3_2_235_2","first-page":"459","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Qiu Jiezhong","year":"2018","unstructured":"Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 459\u2013467."},{"key":"e_1_3_2_236_2","first-page":"5436","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Qiu Zhenyu","year":"2020","unstructured":"Zhenyu Qiu, Wenbin Hu, Jia Wu, Weiwei Liu, Bo Du, and Xiaohua Jia. 2020. Temporal network embedding with high-order nonlinear information. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5436\u20135443."},{"key":"e_1_3_2_237_2","first-page":"2693","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Qu Xiaoru","year":"2020","unstructured":"Xiaoru Qu, Zhao Li, Jialin Wang, Zhipeng Zhang, Pengcheng Zou, Junxiao Jiang, Jiaming Huang, Rong Xiao, Ji Zhang, and Jun Gao. 2020. Category-aware graph neural networks for improving e-commerce review helpfulness prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2693\u20132700."},{"issue":"1","key":"e_1_3_2_238_2","first-page":"1","article-title":"Quantum chemistry structures and properties of 134 kilo molecules","volume":"1","author":"Ramakrishnan Raghunathan","year":"2014","unstructured":"Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole Von Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data 1, 1 (2014), 1\u20137.","journal-title":"Scientific Data"},{"key":"e_1_3_2_239_2","volume-title":"KDD","author":"Ribeiro Leonardo","year":"2017","unstructured":"Leonardo Ribeiro, Pedro Saverese, and Daniel Figueiredo. 2017. struc2vec: Learning node representations from structural identity. In KDD."},{"key":"e_1_3_2_240_2","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1145\/3459637.3482464","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Ringsquandl Martin","year":"2021","unstructured":"Martin Ringsquandl, Houssem Sellami, Marcel Hildebrandt, Dagmar Beyer, Sylwia Henselmeyer, Sebastian Weber, and Mitchell Joblin. 2021. Power to the relational inductive bias: Graph neural networks in electrical power grids. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1538\u20131547."},{"key":"e_1_3_2_241_2","volume-title":"International Conference on Learning Representations","author":"Rong Yu","year":"2019","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. DropEdge: Towards deep graph convolutional networks on node classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_242_2","article-title":"Temporal graph networks for deep learning on dynamic graphs","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020).","journal-title":"arXiv preprint arXiv:2006.10637"},{"key":"e_1_3_2_243_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1145\/3336191.3371843","volume-title":"Proceedings of the 13th International Conference on Web Search and Data Mining","author":"Rossi Ryan A.","year":"2020","unstructured":"Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, and Yasin Abbasi-Yadkori. 2020. A structural graph representation learning framework. In Proceedings of the 13th International Conference on Web Search and Data Mining. 483\u2013491."},{"key":"e_1_3_2_244_2","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1145\/3336191.3371845","volume-title":"Proceedings of the 13th International Conference on Web Search and Data Mining","author":"Sankar Aravind","year":"2020","unstructured":"Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2020. DySAT: Deep neural representation learning on dynamic graphs via self-attention networks. In Proceedings of the 13th International Conference on Web Search and Data Mining. 519\u2013527."},{"key":"e_1_3_2_245_2","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1137\/1.9781611976700.38","volume-title":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","author":"Sato Ryoma","year":"2021","unstructured":"Ryoma Sato, Makoto Yamada, and Hisashi Kashima. 2021. Random features strengthen graph neural networks. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). SIAM, 333\u2013341."},{"key":"e_1_3_2_246_2","first-page":"9323","volume-title":"International Conference on Machine Learning","author":"Satorras V\u0131ctor Garcia","year":"2021","unstructured":"V\u0131ctor Garcia Satorras, Emiel Hoogeboom, and Max Welling. 2021. E (n) equivariant graph neural networks. In International Conference on Machine Learning. PMLR, 9323\u20139332."},{"issue":"1","key":"e_1_3_2_247_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE Transactions on Neural Networks 20, 1 (2008), 61\u201380.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"e_1_3_2_248_2","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15. Springer, 593\u2013607."},{"key":"e_1_3_2_249_2","first-page":"362","volume-title":"International Conference on Neural Information Processing","author":"Seo Youngjoo","year":"2018","unstructured":"Youngjoo Seo, Micha\u00ebl Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured sequence modeling with graph convolutional recurrent networks. In International Conference on Neural Information Processing. Springer, 362\u2013373."},{"key":"e_1_3_2_250_2","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/ICDM.2008.125","volume-title":"2008 8th IEEE International Conference on Data Mining","author":"Sharan Umang","year":"2008","unstructured":"Umang Sharan and Jennifer Neville. 2008. Temporal-relational classifiers for prediction in evolving domains. In 2008 8th IEEE International Conference on Data Mining. IEEE, 540\u2013549."},{"key":"e_1_3_2_251_2","first-page":"3549","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","author":"Shen Xiaobo","year":"2018","unstructured":"Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew-Soon Ong, and Quan-Sen Sun. 2018. Discrete network embedding. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3549\u20133555."},{"key":"e_1_3_2_252_2","first-page":"4605","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Singer Uriel","year":"2019","unstructured":"Uriel Singer, Ido Guy, and Kira Radinsky. 2019. Node embedding over temporal graphs. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 4605\u20134612."},{"key":"e_1_3_2_253_2","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2021.3082932","article-title":"Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey","author":"Skardinga Joakim","year":"2021","unstructured":"Joakim Skardinga, Bogdan Gabrys, and Katarzyna Musial. 2021. Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey. IEEE Access (2021).","journal-title":"IEEE Access"},{"key":"e_1_3_2_254_2","first-page":"914","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Song Chao","year":"2020","unstructured":"Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2020. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 914\u2013921."},{"key":"e_1_3_2_255_2","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1145\/1644893.1644932","volume-title":"Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement","author":"Song Han Hee","year":"2009","unstructured":"Han Hee Song, Tae Won Cho, Vacha Dave, Yin Zhang, and Lili Qiu. 2009. Scalable proximity estimation and link prediction in online social networks. In Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement. 322\u2013335."},{"key":"e_1_3_2_256_2","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1145\/3534678.3539192","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Song Xiran","year":"2022","unstructured":"Xiran Song, Jianxun Lian, Hong Huang, Mingqi Wu, Hai Jin, and Xing Xie. 2022. Friend recommendations with self-rescaling graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3909\u20133919."},{"key":"e_1_3_2_257_2","first-page":"13714","article-title":"Convolutional tensor-train LSTM for spatio-temporal learning","volume":"33","author":"Su Jiahao","year":"2020","unstructured":"Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, and Anima Anandkumar. 2020. Convolutional tensor-train LSTM for spatio-temporal learning. Advances in Neural Information Processing Systems 33 (2020), 13714\u201313726.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_258_2","first-page":"5892","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Sun Ke","year":"2020","unstructured":"Ke Sun, Zhouchen Lin, and Zhanxing Zhu. 2020. Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5892\u20135899."},{"key":"e_1_3_2_259_2","first-page":"725","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Sun Xiangguo","year":"2021","unstructured":"Xiangguo Sun, Hongzhi Yin, Bo Liu, Hongxu Chen, Jiuxin Cao, Yingxia Shao, and Nguyen Quoc Viet Hung. 2021. Heterogeneous hypergraph embedding for graph classification. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 725\u2013733."},{"key":"e_1_3_2_260_2","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1145\/3447548.3467373","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, and Jianzhu Ma. 2021. Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1541\u20131551."},{"key":"e_1_3_2_261_2","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1145\/2736277.2741093","volume-title":"Proceedings of the 24th WWW","author":"Tang Jian","year":"2015","unstructured":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th WWW. International World Wide Web Conferences Steering Committee, 1067\u20131077."},{"key":"e_1_3_2_262_2","article-title":"Autobahn: Automorphism-based graph neural nets","volume":"34","author":"Thiede Erik","year":"2021","unstructured":"Erik Thiede, Wenda Zhou, and Risi Kondor. 2021. Autobahn: Automorphism-based graph neural nets. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_263_2","article-title":"Understanding over-squashing and bottlenecks on graphs via curvature","author":"Topping Jake","year":"2022","unstructured":"Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein. 2022. Understanding over-squashing and bottlenecks on graphs via curvature. International Conference on Learning Representations (2022).","journal-title":"International Conference on Learning Representations"},{"key":"e_1_3_2_264_2","volume-title":"International Conference on Learning Representations","author":"Trivedi Rakshit","year":"2019","unstructured":"Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. DyRep: Learning representations over dynamic graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_265_2","first-page":"539","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Tsitsulin Anton","year":"2018","unstructured":"Anton Tsitsulin, Davide Mottin, Panagiotis Karras, and Emmanuel M\u00fcller. 2018. VERSE: Versatile graph embeddings from similarity measures. In Proceedings of the 2018 World Wide Web Conference. 539\u2013548."},{"key":"e_1_3_2_266_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_267_2","volume-title":"International Conference on Learning Representations","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations."},{"issue":"3","key":"e_1_3_2_268_2","first-page":"4","article-title":"Deep Graph Infomax.","volume":"2","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic, William Fedus, William L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep Graph Infomax. ICLR (Poster) 2, 3 (2019), 4.","journal-title":"ICLR (Poster)"},{"key":"e_1_3_2_269_2","first-page":"14143","article-title":"Building powerful and equivariant graph neural networks with structural message-passing","volume":"33","author":"Vignac Clement","year":"2020","unstructured":"Clement Vignac, Andreas Loukas, and Pascal Frossard. 2020. Building powerful and equivariant graph neural networks with structural message-passing. Advances in Neural Information Processing Systems 33 (2020), 14143\u201314155.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_270_2","first-page":"1225","volume-title":"Proceedings of the 22nd ACM SIGKDD","author":"Wang Daixin","year":"2016","unstructured":"Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD. ACM, 1225\u20131234."},{"key":"e_1_3_2_271_2","volume-title":"IJCAI","author":"Wang Guangtao","year":"2021","unstructured":"Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec. 2021. Multi-hop attention graph neural networks. In IJCAI."},{"key":"e_1_3_2_272_2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/ICDM.2018.00071","volume-title":"2018 IEEE International Conference on Data Mining (ICDM)","author":"Wang Hao","year":"2018","unstructured":"Hao Wang, Enhong Chen, Qi Liu, Tong Xu, Dongfang Du, Wen Su, and Xiaopeng Zhang. 2018. A united approach to learning sparse attributed network embedding. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 557\u2013566."},{"key":"e_1_3_2_273_2","volume-title":"International Conference on Learning Representations","author":"Wang Haorui","year":"2021","unstructured":"Haorui Wang, Haoteng Yin, Muhan Zhang, and Pan Li. 2021. Equivariant and stable positional encoding for more powerful graph neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_274_2","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1145\/3340531.3411963","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Wang Junshan","year":"2020","unstructured":"Junshan Wang, Guojie Song, Yi Wu, and Liang Wang. 2020. Streaming graph neural networks via continual learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1515\u20131524."},{"key":"e_1_3_2_275_2","article-title":"CausalGNN: Causal-based graph neural networks for spatio-temporal epidemic forecasting","author":"Wang Lijing","year":"2022","unstructured":"Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe. 2022. CausalGNN: Causal-based graph neural networks for spatio-temporal epidemic forecasting. Proceedings of the AAAI Conference on Artificial Intelligence (2022).","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_276_2","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1145\/3394486.3403284","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Wang Menghan","year":"2020","unstructured":"Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, and Xiao-ming Wu. 2020. M2GRL: A multi-task multi-view graph representation learning framework for web-scale recommender systems. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2349\u20132358."},{"key":"e_1_3_2_277_2","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et\u00a0al. 2019. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019).","journal-title":"arXiv preprint arXiv:1909.01315"},{"key":"e_1_3_2_278_2","doi-asserted-by":"crossref","DOI":"10.5772\/intechopen.95124","article-title":"Powerful graph convolutioal networks with adaptive propagation mechanism for homophily and heterophily","author":"Wang Tao","year":"2022","unstructured":"Tao Wang, Rui Wang, Di Jin, Dongxiao He, and Yuxiao Huang. 2022. Powerful graph convolutioal networks with adaptive propagation mechanism for homophily and heterophily. Proceedings of the AAAI Conference on Artificial Intelligence (2022).","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_279_2","article-title":"A survey on heterogeneous graph embedding: Methods, techniques, applications and sources","author":"Wang Xiao","year":"2022","unstructured":"Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and S. Yu Philip. 2022. A survey on heterogeneous graph embedding: Methods, techniques, applications and sources. IEEE Transactions on Big Data (2022).","journal-title":"IEEE Transactions on Big Data"},{"key":"e_1_3_2_280_2","volume-title":"31st AAAI Conference on Artificial Intelligence","author":"Wang Xiao","year":"2017","unstructured":"Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017. Community preserving network embedding. In 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_281_2","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1145\/3308558.3313562","volume-title":"The World Wide Web Conference","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous graph attention network. In The World Wide Web Conference. 2022\u20132032."},{"key":"e_1_3_2_282_2","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1145\/3366423.3380186","volume-title":"Proceedings of The Web Conference 2020","author":"Wang Xiaoyang","year":"2020","unstructured":"Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of The Web Conference 2020. 1082\u20131092."},{"key":"e_1_3_2_283_2","volume-title":"International Conference on Learning Representations","author":"Wang Yanbang","year":"2020","unstructured":"Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2020. Inductive representation learning in temporal networks via causal anonymous walks. In International Conference on Learning Representations."},{"key":"e_1_3_2_284_2","article-title":"PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs","volume":"30","author":"Wang Yunbo","year":"2017","unstructured":"Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S. Yu. 2017. PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_285_2","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1145\/3340531.3412041","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Wang Yaojing","year":"2020","unstructured":"Yaojing Wang, Guosheng Pan, Yuan Yao, Hanghang Tong, Hongxia Yang, Feng Xu, and Jian Lu. 2020. Bringing order to network embedding: A relative ranking based approach. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1585\u20131594."},{"key":"e_1_3_2_286_2","first-page":"1042","volume-title":"Artificial Intelligence and Statistics","author":"Wang Yu-Xiang","year":"2015","unstructured":"Yu-Xiang Wang, James Sharpnack, Alex Smola, and Ryan Tibshirani. 2015. Trend filtering on graphs. In Artificial Intelligence and Statistics. PMLR, 1042\u20131050."},{"key":"e_1_3_2_287_2","article-title":"AutoGEL: An automated graph neural network with explicit link information","volume":"34","author":"Wang Zhili","year":"2021","unstructured":"Zhili Wang, Shimin Di, and Lei Chen. 2021. AutoGEL: An automated graph neural network with explicit link information. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_288_2","doi-asserted-by":"crossref","first-page":"2060","DOI":"10.1145\/3459637.3482482","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Wang Zhaonan","year":"2021","unstructured":"Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, and Ryosuke Shibasaki. 2021. Spatio-temporal-categorical graph neural networks for fine-grained multi-incident co-prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2060\u20132069."},{"key":"e_1_3_2_289_2","volume-title":"International Conference on Learning Representations","author":"Wijesinghe Asiri","year":"2021","unstructured":"Asiri Wijesinghe and Qing Wang. 2021. A new perspective on \u201cHow graph neural networks go beyond Weisfeiler-Lehman?\u201d. In International Conference on Learning Representations."},{"key":"e_1_3_2_290_2","article-title":"A new perspective on \u201cHow graph neural networks go beyond Weisfeiler-Lehman?\u201d","author":"Wijesinghe Asiri","year":"2022","unstructured":"Asiri Wijesinghe and Qing Wang. 2022. A new perspective on \u201cHow graph neural networks go beyond Weisfeiler-Lehman?\u201d. ICML (2022).","journal-title":"ICML"},{"key":"e_1_3_2_291_2","first-page":"6861","volume-title":"International Conference on Machine Learning","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International Conference on Machine Learning. PMLR, 6861\u20136871."},{"key":"e_1_3_2_292_2","first-page":"774","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Wu Fan","year":"2019","unstructured":"Fan Wu and Lixia Wu. 2019. DeepETA: A spatial-temporal sequential neural network model for estimating time of arrival in package delivery system. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 774\u2013781."},{"key":"e_1_3_2_293_2","first-page":"13266","article-title":"Representing long-range context for graph neural networks with global attention","volume":"34","author":"Wu Zhanghao","year":"2021","unstructured":"Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E. Gonzalez, and Ion Stoica. 2021. Representing long-range context for graph neural networks with global attention. Advances in Neural Information Processing Systems 34 (2021), 13266\u201313279.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_294_2","article-title":"A comprehensive survey on graph neural networks","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems (2020).","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_295_2","volume-title":"IJCAI","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for deep spatial-temporal graph modeling. In IJCAI."},{"issue":"2","key":"e_1_3_2_296_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","article-title":"Graph learning: A survey","volume":"2","author":"Xia Feng","year":"2021","unstructured":"Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, and Huan Liu. 2021. Graph learning: A survey. IEEE Transactions on Artificial Intelligence 2, 2 (2021), 109\u2013127.","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"e_1_3_2_297_2","first-page":"70","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Xia Lianghao","year":"2022","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Huang. 2022. Hypergraph contrastive collaborative filtering. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 70\u201379."},{"key":"e_1_3_2_298_2","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1137\/1.9781611976236.67","volume-title":"Proceedings of the 2020 SIAM International Conference on Data Mining","author":"Xiao Wenyi","year":"2020","unstructured":"Wenyi Xiao, Huan Zhao, Vincent W. Zheng, and Yangqiu Song. 2020. Vertex-reinforced random walk for network embedding. In Proceedings of the 2020 SIAM International Conference on Data Mining. SIAM, 595\u2013603."},{"key":"e_1_3_2_299_2","article-title":"A survey on dynamic network embedding","author":"Xie Yu","year":"2020","unstructured":"Yu Xie, Chunyi Li, Bin Yu, Chen Zhang, and Zhouhua Tang. 2020. A survey on dynamic network embedding. arXiv preprint arXiv:2006.08093 (2020).","journal-title":"arXiv preprint arXiv:2006.08093"},{"key":"e_1_3_2_300_2","first-page":"3947","volume-title":"IJCAI","author":"Xu Dongkuan","year":"2019","unstructured":"Dongkuan Xu, Wei Cheng, Dongsheng Luo, Xiao Liu, and Xiang Zhang. 2019. Spatio-temporal attentive RNN for node classification in temporal attributed graphs. In IJCAI. 3947\u20133953."},{"key":"e_1_3_2_301_2","article-title":"Inductive representation learning on temporal graphs","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. ICLR (2020).","journal-title":"ICLR"},{"key":"e_1_3_2_302_2","article-title":"Automorphic equivalence-aware graph neural network","volume":"34","author":"Xu Fengli","year":"2021","unstructured":"Fengli Xu, Quanming Yao, Pan Hui, and Yong Li. 2021. Automorphic equivalence-aware graph neural network. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_303_2","first-page":"3356","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Xu Jixing","year":"2020","unstructured":"Jixing Xu, Zhenlong Zhu, Jianxin Zhao, Xuanye Liu, Minghui Shan, and Jiecheng Guo. 2020. Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3356\u20133365."},{"key":"e_1_3_2_304_2","volume-title":"International Conference on Learning Representations","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_305_2","first-page":"5453","volume-title":"International Conference on Machine Learning","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In International Conference on Machine Learning. PMLR, 5453\u20135462."},{"key":"e_1_3_2_306_2","first-page":"11592","volume-title":"International Conference on Machine Learning","author":"Xu Keyulu","year":"2021","unstructured":"Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi. 2021. Optimization of graph neural networks: Implicit acceleration by skip connections and more depth. In International Conference on Machine Learning. PMLR, 11592\u201311602."},{"issue":"4","key":"e_1_3_2_307_2","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1137\/20M1386062","article-title":"Understanding graph embedding methods and their applications","volume":"63","author":"Xu Mengjia","year":"2021","unstructured":"Mengjia Xu. 2021. Understanding graph embedding methods and their applications. SIAM Rev. 63, 4 (2021), 825\u2013853.","journal-title":"SIAM Rev."},{"key":"e_1_3_2_308_2","first-page":"1848","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Yan Bencheng","year":"2020","unstructured":"Bencheng Yan, Chaokun Wang, Gaoyang Guo, and Yunkai Lou. 2020. TinyGNN: Learning efficient graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1848\u20131856."},{"key":"e_1_3_2_309_2","first-page":"3894","volume-title":"IJCAI","author":"Yang Cheng","year":"2017","unstructured":"Cheng Yang, Maosong Sun, Zhiyuan Liu, and Cunchao Tu. 2017. Fast network embedding enhancement via high order proximity approximation. In IJCAI, Vol. 17. 3894\u20133900."},{"issue":"10","key":"e_1_3_2_310_2","doi-asserted-by":"crossref","first-page":"4854","DOI":"10.1109\/TKDE.2020.3045924","article-title":"Heterogeneous network representation learning: A unified framework with survey and benchmark","volume":"34","author":"Yang Carl","year":"2020","unstructured":"Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han. 2020. Heterogeneous network representation learning: A unified framework with survey and benchmark. IEEE Transactions on Knowledge and Data Engineering 34, 10 (2020), 4854\u20134873.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_311_2","first-page":"2976","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","author":"Yang Guolei","year":"2018","unstructured":"Guolei Yang, Ying Cai, and Chandan K. Reddy. 2018. Spatio-temporal check-in time prediction with recurrent neural network based survival analysis. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2976\u20132983."},{"key":"e_1_3_2_312_2","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/ICDM.2018.8626170","volume-title":"2018 IEEE International Conference on Data Mining (ICDM)","author":"Yang Hong","year":"2018","unstructured":"Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, and Chengqi Zhang. 2018. Binarized attributed network embedding. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 1476\u20131481."},{"key":"e_1_3_2_313_2","article-title":"Diverse message passing for attribute with heterophily","volume":"34","author":"Yang Liang","year":"2021","unstructured":"Liang Yang, Mengzhe Li, Liyang Liu, Chuan Wang, Xiaochun Cao, Yuanfang Guo, et\u00a0al. 2021. Diverse message passing for attribute with heterophily. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_314_2","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1109\/ICDM50108.2020.00082","volume-title":"2020 IEEE International Conference on Data Mining (ICDM)","author":"Yang Menglin","year":"2020","unstructured":"Menglin Yang, Ziqiao Meng, and Irwin King. 2020. FeatureNorm: L2 feature normalization for dynamic graph embedding. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 731\u2013740."},{"key":"e_1_3_2_315_2","article-title":"Graph pointer neural networks","author":"Yang Tianmeng","year":"2022","unstructured":"Tianmeng Yang, Yujing Wang, Zhihan Yue, Yaming Yang, Yunhai Tong, and Jing Bai. 2022. Graph pointer neural networks. Proceedings of the AAAI Conference on Artificial Intelligence (2022).","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_316_2","article-title":"Interpretable and efficient heterogeneous graph convolutional network","author":"Yang Yaming","year":"2021","unstructured":"Yaming Yang, Ziyu Guan, Jianxin Li, Wei Zhao, Jiangtao Cui, and Quan Wang. 2021. Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering (2021).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_317_2","first-page":"5668","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Yao Huaxiu","year":"2019","unstructured":"Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5668\u20135675."},{"key":"e_1_3_2_318_2","volume-title":"International Joint Conference on Artificial Intelligence","author":"Yao Kai-Lang","year":"2021","unstructured":"Kai-Lang Yao and Wu-Jun Li. 2021. Blocking-based neighbor sampling for large-scale graph neural networks. In International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_2_319_2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.procs.2016.04.102","article-title":"Link prediction based on common-neighbors for dynamic social network","volume":"83","author":"Yao Lin","year":"2016","unstructured":"Lin Yao, Luning Wang, Lv Pan, and Kai Yao. 2016. Link prediction based on common-neighbors for dynamic social network. Procedia Computer Science 83 (2016), 82\u201389.","journal-title":"Procedia Computer Science"},{"key":"e_1_3_2_320_2","first-page":"305","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Ye Junchen","year":"2019","unstructured":"Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, and Hui Xiong. 2019. Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 305\u2013313."},{"key":"e_1_3_2_321_2","first-page":"11975","volume-title":"International Conference on Machine Learning","author":"Yehudai Gilad","year":"2021","unstructured":"Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron. 2021. From local structures to size generalization in graph neural networks. In International Conference on Machine Learning. PMLR, 11975\u201311986."},{"key":"e_1_3_2_322_2","first-page":"513","volume-title":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","author":"Yi Peiyu","year":"2021","unstructured":"Peiyu Yi, Feihu Huang, and Jian Peng. 2021. A fine-grained graph-based spatiotemporal network for bike flow prediction in bike-sharing systems. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). SIAM, 513\u2013521."},{"key":"e_1_3_2_323_2","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1145\/3292500.3330860","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Yin Yuan","year":"2019","unstructured":"Yuan Yin and Zhewei Wei. 2019. Scalable graph embeddings via sparse transpose proximities. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1429\u20131437."},{"key":"e_1_3_2_324_2","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1145\/3219819.3219890","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Ying Rex","year":"2018","unstructured":"Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 974\u2013983."},{"key":"e_1_3_2_325_2","article-title":"Hierarchical graph representation learning with differentiable pooling","volume":"31","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_326_2","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1145\/3447548.3467284","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Yoon Minji","year":"2021","unstructured":"Minji Yoon, Th\u00e9ophile Gervet, Baoxu Shi, Sufeng Niu, Qi He, and Jaewon Yang. 2021. Performance-adaptive sampling strategy towards fast and accurate graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2046\u20132056."},{"key":"e_1_3_2_327_2","first-page":"10737","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"You Jiaxuan","year":"2021","unstructured":"Jiaxuan You, Jonathan M. Gomes-Selman, Rex Ying, and Jure Leskovec. 2021. Identity-aware graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 10737\u201310745."},{"key":"e_1_3_2_328_2","first-page":"7134","volume-title":"International Conference on Machine Learning","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You, Rex Ying, and Jure Leskovec. 2019. Position-aware graph neural networks. In International Conference on Machine Learning. PMLR, 7134\u20137143."},{"key":"e_1_3_2_329_2","first-page":"17009","article-title":"Design space for graph neural networks","volume":"33","author":"You Jiaxuan","year":"2020","unstructured":"Jiaxuan You, Zhitao Ying, and Jure Leskovec. 2020. Design space for graph neural networks. Advances in Neural Information Processing Systems 33 (2020), 17009\u201317021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_330_2","first-page":"3634","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","author":"Yu Bing","year":"2018","unstructured":"Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3634\u20133640."},{"key":"e_1_3_2_331_2","first-page":"1707","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"Yu Chia-An","year":"2018","unstructured":"Chia-An Yu, Ching-Lun Tai, Tak-Shing Chan, and Yi-Hsuan Yang. 2018. Modeling multi-way relations with hypergraph embedding. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1707\u20131710."},{"key":"e_1_3_2_332_2","first-page":"2084","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Yu Junliang","year":"2021","unstructured":"Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-aware self-supervised tri-training for recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2084\u20132092."},{"key":"e_1_3_2_333_2","first-page":"1294","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yu Junliang","year":"2022","unstructured":"Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1294\u20131303."},{"key":"e_1_3_2_334_2","first-page":"2672","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Yu Wenchao","year":"2018","unstructured":"Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. 2018. NetWalk: A flexible deep embedding approach for anomaly detection in dynamic networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2672\u20132681."},{"key":"e_1_3_2_335_2","article-title":"Neo-GNNs: Neighborhood overlap-aware graph neural networks for link prediction","volume":"34","author":"Yun Seongjun","year":"2021","unstructured":"Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, and Hyunwoo J. Kim. 2021. Neo-GNNs: Neighborhood overlap-aware graph neural networks for link prediction. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_336_2","doi-asserted-by":"crossref","first-page":"123717","DOI":"10.1109\/ACCESS.2021.3110845","article-title":"Identifying protein complexes in protein-protein interaction data using graph convolutional network","volume":"9","author":"Zaki Nazar","year":"2021","unstructured":"Nazar Zaki, Harsh Singh, and Elfadil A. Mohamed. 2021. Identifying protein complexes in protein-protein interaction data using graph convolutional network. IEEE Access 9 (2021), 123717\u2013123726.","journal-title":"IEEE Access"},{"key":"e_1_3_2_337_2","first-page":"10968","volume-title":"International Conference on Machine Learning","author":"Zambon Daniele","year":"2020","unstructured":"Daniele Zambon, Cesare Alippi, and Lorenzo Livi. 2020. Graph random neural features for distance-preserving graph representations. In International Conference on Machine Learning. PMLR, 10968\u201310977."},{"key":"e_1_3_2_338_2","article-title":"Decoupling the depth and scope of graph neural networks","volume":"34","author":"Zeng Hanqing","year":"2021","unstructured":"Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, and Ren Chen. 2021. Decoupling the depth and scope of graph neural networks. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_339_2","volume-title":"International Conference on Learning Representations","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. GraphSAINT: Graph sampling based inductive learning method. In International Conference on Learning Representations."},{"key":"e_1_3_2_340_2","volume-title":"IJCAI","author":"Zha Daochen","year":"2019","unstructured":"Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, and Xia Hu. 2019. Experience replay optimization. In IJCAI."},{"key":"e_1_3_2_341_2","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1145\/3292500.3330961","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Zhang Chuxu","year":"2019","unstructured":"Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 793\u2013803."},{"issue":"1","key":"e_1_3_2_342_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TBDATA.2018.2850013","article-title":"Network representation learning: A survey","volume":"6","author":"Zhang Daokun","year":"2018","unstructured":"Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018. Network representation learning: A survey. IEEE Transactions on Big Data 6, 1 (2018), 3\u201328.","journal-title":"IEEE Transactions on Big Data"},{"key":"e_1_3_2_343_2","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1145\/3459637.3482465","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Zhang Jianfei","year":"2021","unstructured":"Jianfei Zhang, Ai-Te Kuo, Jianan Zhao, Qianlong Wen, Erin Winstanley, Chuxu Zhang, and Yanfang Ye. 2021. RxNet: Rx-refill graph neural network for overprescribing detection. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2537\u20132546."},{"key":"e_1_3_2_344_2","volume-title":"31st AAAI Conference on Artificial Intelligence","author":"Zhang Junbo","year":"2017","unstructured":"Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_345_2","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_346_2","volume-title":"Thirty-second AAAI Conference on Artificial Intelligence","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018. An end-to-end deep learning architecture for graph classification. In Thirty-second AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_347_2","article-title":"Nested graph neural networks","volume":"34","author":"Zhang Muhan","year":"2021","unstructured":"Muhan Zhang and Pan Li. 2021. Nested graph neural networks. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_348_2","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1109\/ICDM51629.2021.00191","volume-title":"2021 IEEE International Conference on Data Mining (ICDM)","author":"Zhang Mingyang","year":"2021","unstructured":"Mingyang Zhang, Yong Li, Funing Sun, Diansheng Guo, and Pan Hui. 2021. Adaptive spatio-temporal convolutional network for traffic prediction. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 1475\u20131480."},{"key":"e_1_3_2_349_2","first-page":"1177","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Zhang Qi","year":"2020","unstructured":"Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2020. Spatio-temporal graph structure learning for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1177\u20131185."},{"key":"e_1_3_2_350_2","first-page":"1395","volume-title":"IJCAI: Proceedings of the Conference","volume":"2020","author":"Zhang Shuo","year":"2020","unstructured":"Shuo Zhang and Lei Xie. 2020. Improving attention mechanism in graph neural networks via cardinality preservation. In IJCAI: Proceedings of the Conference, Vol. 2020. NIH Public Access, 1395."},{"key":"e_1_3_2_351_2","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1145\/3340531.3412119","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Zhang Weifeng","year":"2020","unstructured":"Weifeng Zhang, Jingwen Mao, Yi Cao, and Congfu Xu. 2020. Multiplex graph neural networks for multi-behavior recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2313\u20132316."},{"key":"e_1_3_2_352_2","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1145\/3340531.3411941","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Zhang Xiyue","year":"2020","unstructured":"Xiyue Zhang, Chao Huang, Yong Xu, and Lianghao Xia. 2020. Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1853\u20131862."},{"key":"e_1_3_2_353_2","first-page":"15008","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Zhang Xiyue","year":"2021","unstructured":"Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, and Yu Zheng. 2021. Traffic flow forecasting with spatial-temporal graph diffusion network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 15008\u201315015."},{"key":"e_1_3_2_354_2","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1145\/3447548.3467296","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Zhang Xingyi","year":"2021","unstructured":"Xingyi Zhang, Kun Xie, Sibo Wang, and Zengfeng Huang. 2021. Learning based proximity matrix factorization for node embedding. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2243\u20132253."},{"key":"e_1_3_2_355_2","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/ICDM.2018.00094","volume-title":"2018 IEEE International Conference on Data Mining (ICDM)","author":"Zhang Ziwei","year":"2018","unstructured":"Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, and Wenwu Zhu. 2018. Billion-scale network embedding with iterative random projection. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 787\u2013796."},{"key":"e_1_3_2_356_2","volume-title":"32nd AAAI Conference on Artificial Intelligence","author":"Zhang Ziwei","year":"2018","unstructured":"Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, and Wenwu Zhu. 2018. Timers: Error-bounded SVD restart on dynamic networks. In 32nd AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_357_2","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.1145\/3219819.3219969","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Zhang Ziwei","year":"2018","unstructured":"Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao, and Wenwu Zhu. 2018. Arbitrary-order proximity preserved network embedding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2778\u20132786."},{"key":"e_1_3_2_358_2","article-title":"Deep learning on graphs: A survey","author":"Zhang Ziwei","year":"2020","unstructured":"Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering (2020).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_359_2","first-page":"8577","article-title":"Factor graph neural networks","volume":"33","author":"Zhang Zhen","year":"2020","unstructured":"Zhen Zhang, Fan Wu, and Wee Sun Lee. 2020. Factor graph neural networks. Advances in Neural Information Processing Systems 33 (2020), 8577\u20138587.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_360_2","article-title":"Adaptive diffusion in graph neural networks","volume":"34","author":"Zhao Jialin","year":"2021","unstructured":"Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, and Jie Tang. 2021. Adaptive diffusion in graph neural networks. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_361_2","first-page":"4697","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Zhao Jianan","year":"2021","unstructured":"Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye. 2021. Heterogeneous graph structure learning for graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4697\u20134705."},{"key":"e_1_3_2_362_2","volume-title":"International Conference on Learning Representations","author":"Zhao Lingxiao","year":"2019","unstructured":"Lingxiao Zhao and Leman Akoglu. 2019. PairNorm: Tackling oversmoothing in GNNs. In International Conference on Learning Representations."},{"key":"e_1_3_2_363_2","first-page":"36","volume-title":"2020 IEEE\/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3)","author":"Zheng Da","year":"2020","unstructured":"Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis. 2020. DistDG;: Distributed graph neural network training for billion-scale graphs. In 2020 IEEE\/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3). IEEE, 36\u201344."},{"key":"e_1_3_2_364_2","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1145\/3397271.3401172","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zheng Da","year":"2020","unstructured":"Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, and George Karypis. 2020. DGL-KE: Training knowledge graph embeddings at scale. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 739\u2013748."},{"key":"e_1_3_2_365_2","first-page":"12761","volume-title":"International Conference on Machine Learning","author":"Zheng Xuebin","year":"2021","unstructured":"Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Li\u00f3, Ming Li, and Guido Montufar. 2021. How framelets enhance graph neural networks. In International Conference on Machine Learning. PMLR, 12761\u201312771."},{"issue":"18","key":"e_1_3_2_366_2","first-page":"1","article-title":"Decimated framelet system on graphs and fast G-framelet transforms","volume":"23","author":"Zheng Xuebin","year":"2022","unstructured":"Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, and Xiaosheng Zhuang. 2022. Decimated framelet system on graphs and fast G-framelet transforms. Journal of Machine Learning Research 23, 18 (2022), 1\u201368.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_367_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"31","author":"Zhou Chang","year":"2017","unstructured":"Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, and Jun Gao. 2017. Scalable graph embedding for asymmetric proximity. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31."},{"key":"e_1_3_2_368_2","article-title":"Learning with hypergraphs: Clustering, classification, and embedding","volume":"19","author":"Zhou Dengyong","year":"2006","unstructured":"Dengyong Zhou, Jiayuan Huang, and Bernhard Sch\u00f6lkopf. 2006. Learning with hypergraphs: Clustering, classification, and embedding. Advances in Neural Information Processing Systems 19 (2006).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_369_2","first-page":"4714","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Zhou Fan","year":"2021","unstructured":"Fan Zhou and Chengtai Cao. 2021. Overcoming catastrophic forgetting in graph neural networks with experience replay. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4714\u20134722."},{"key":"e_1_3_2_370_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou Jie","year":"2020","unstructured":"Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1 (2020), 57\u201381.","journal-title":"AI Open"},{"issue":"2","key":"e_1_3_2_371_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3491206","article-title":"Network representation learning: From preprocessing, feature extraction to node embedding","volume":"55","author":"Zhou Jingya","year":"2022","unstructured":"Jingya Zhou, Ling Liu, Wenqi Wei, and Jianxi Fan. 2022. Network representation learning: From preprocessing, feature extraction to node embedding. ACM Computing Surveys (CSUR) 55, 2 (2022), 1\u201335.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_372_2","first-page":"4917","article-title":"Towards deeper graph neural networks with differentiable group normalization","volume":"33","author":"Zhou Kaixiong","year":"2020","unstructured":"Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. Advances in Neural Information Processing Systems 33 (2020), 4917\u20134928.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_373_2","first-page":"1352","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. 2021. Multi-channel graph neural networks. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 1352\u20131358."},{"key":"e_1_3_2_374_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Zhou Lekui","year":"2018","unstructured":"Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_2_375_2","first-page":"1452","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"Zhu Hongmin","year":"2021","unstructured":"Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, and Yongdong Zhang. 2021. Bilinear graph neural network with neighbor interactions. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 1452\u20131458."},{"key":"e_1_3_2_376_2","doi-asserted-by":"crossref","first-page":"3682","DOI":"10.1145\/3459637.3482168","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz. 2021. REFINE: Random range finder for network embedding. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3682\u20133686."},{"key":"e_1_3_2_377_2","first-page":"11168","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Zhu Jiong","year":"2021","unstructured":"Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, and Danai Koutra. 2021. Graph neural networks with heterophily. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 11168\u201311176."},{"key":"e_1_3_2_378_2","first-page":"345","volume-title":"International Conference on Database and Expert Systems Applications","author":"Zhu Jia","year":"2012","unstructured":"Jia Zhu, Qing Xie, and Eun Jung Chin. 2012. A hybrid time-series link prediction framework for large social network. In International Conference on Database and Expert Systems Applications. Springer, 345\u2013359."},{"key":"e_1_3_2_379_2","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems 33 (2020), 7793\u20137804.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_380_2","first-page":"1534","volume-title":"2019 IEEE International Conference on Data Mining (ICDM)","author":"Zhu Shichao","year":"2019","unstructured":"Shichao Zhu, Chuan Zhou, Shirui Pan, Xingquan Zhu, and Bin Wang. 2019. Relation structure-aware heterogeneous graph neural network. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1534\u20131539."},{"key":"e_1_3_2_381_2","article-title":"Neural Bellman-Ford networks: A general graph neural network framework for link prediction","volume":"34","author":"Zhu Zhaocheng","year":"2021","unstructured":"Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. 2021. Neural Bellman-Ford networks: A general graph neural network framework for link prediction. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_382_2","first-page":"499","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Zhuang Chenyi","year":"2018","unstructured":"Chenyi Zhuang and Qiang Ma. 2018. Dual graph convolutional networks for graph-based semi-supervised classification. In Proceedings of the 2018 World Wide Web Conference. 499\u2013508."},{"key":"e_1_3_2_383_2","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","volume":"32","author":"Zou Difan","year":"2019","unstructured":"Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu. 2019. Layer-dependent importance sampling for training deep and large graph convolutional networks. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_384_2","first-page":"2857","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Zuo Yuan","year":"2018","unstructured":"Yuan Zuo, Guannan Liu, Hao Lin, Jia Guo, Xiaoqian Hu, and Junjie Wu. 2018. Embedding temporal network via neighborhood formation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2857\u20132866."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3633518","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3633518","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:09Z","timestamp":1750287009000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3633518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":383,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3633518"],"URL":"https:\/\/doi.org\/10.1145\/3633518","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]},"assertion":[{"value":"2022-06-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}