{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T07:31:25Z","timestamp":1649748685100},"reference-count":327,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62002255, 62076177 and 61972273"]},{"name":"Shanxi Key Core Technology and Generic Technology Research and Development Special Project","award":["2020XXX007"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.<\/jats:p>","DOI":"10.1145\/3495161","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T09:58:29Z","timestamp":1641808709000},"page":"1-54","source":"Crossref","is-referenced-by-count":1,"title":["Graph Neural Networks: Taxonomy, Advances, and Trends"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0304-0863","authenticated-orcid":false,"given":"Yu","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Data Science\/Shanxi Spatial Information Network Engineering Technology Research Center, Taiyuan University of Technology, Taiyuan, Shanxi, China"}]},{"given":"Haixia","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Data Science\/Shanxi Spatial Information Network Engineering Technology Research Center, Taiyuan University of Technology, Taiyuan, Shanxi, China"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China"}]},{"given":"Shufeng","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China"}]},{"given":"Dengao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Data Science\/Shanxi Spatial Information Network Engineering Technology Research Center, Taiyuan University of Technology, Taiyuan, Shanxi, China"}]},{"given":"Jumin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information and Computer\/Shanxi Intelligent Perception Engineering Research Center, Taiyuan University of Technology, Taiyuan, Shanxi, China,"}]}],"member":"320","reference":[{"key":"e_1_3_2_2_2","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan Trevor Kileen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf Edward Yang, Zach Devito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. arXiv (2019). https:\/\/arxiv.org\/abs\/1912.01703.","journal-title":"arXiv"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_4_2","first-page":"2009","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201918)","author":"Rahimi Afshin","year":"2018","unstructured":"Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised user geolocation via graph convolutional networks. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201918). ACL, 2009\u20132019."},{"key":"e_1_3_2_5_2","first-page":"6530","volume-title":"The International Conference on Neural Information Processing Systems","author":"Fout Alex","year":"2017","unstructured":"Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur. 2017. Protein interface prediction using graph convolutional networks. In The International Conference on Neural Information Processing Systems. Curran Associates, Inc., 6530\u20136539."},{"key":"e_1_3_2_6_2","unstructured":"Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. (2009). https:\/\/www.cs.toronto.edu\/kriz\/learning-features-2009-TR.pdf."},{"key":"e_1_3_2_7_2","first-page":"4470","volume-title":"The International Conference on Machine Learning (ICML\u201918)","volume":"80","author":"Sanchez-Gonzalez Alvaro","year":"2018","unstructured":"Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia. 2018. Graph networks as learnable physics engines for inference and control. In The International Conference on Machine Learning (ICML\u201918), Vol. 80. PMLR, 4470\u20134479."},{"key":"e_1_3_2_8_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Khasahmadi Amir H.","year":"2020","unstructured":"Amir H. Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, and Quaid Morris. 2020. Memory-based graph networks. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_9_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Loukas Andreas","year":"2020","unstructured":"Andreas Loukas. 2020. What graph neural networks cannot learn-depth vs width. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00049"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053451"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"e_1_3_2_13_2","first-page":"5998","volume-title":"The International Conference on Neural Information Processing (NIPS\u201917)","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomiz, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In The International Conference on Neural Information Processing (NIPS\u201917). Curran Associates, Inc., Long Beach, CA, 5998\u20136008."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1970.tb01770.x"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1282"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_17_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Xu Bingbing","year":"2019","unstructured":"Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng. 2019. Graph wavelet neural network. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1549"},{"key":"e_1_3_2_19_2","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","volume":"32","author":"Bojchevski Aleksandar","year":"2019","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Certifiable robustness to graph perturbations. In The International Conference on Neural Information Processing Systems (NeurPS\u201919), Vol. 32. Curran Associates, Inc."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_21_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Gulcehre Caglar","year":"2019","unstructured":"Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, and Nando de Freitas. 2019. Hyperbolic attention networks. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403293"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3045924"},{"key":"e_1_3_2_24_2","first-page":"942","volume-title":"The 35th International Conference on Machine Learning (ICML\u201918)","volume":"80","author":"Chen Jianfei","year":"2018","unstructured":"Jianfei Chen, Jun Zhu, and Le Song. 2018. Stochastic training of graph convolutional networks with variance reduction. In The 35th International Conference on Machine Learning (ICML\u201918), Vol. 80. PMLR, 942\u2013950."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/171"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449957"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186116"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00132"},{"key":"e_1_3_2_29_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Zhang Chris","year":"2019","unstructured":"Chris Zhang, Mengye Ren, and Raquel Urtasun. 2019. Graph hypernetworks for neural architecture search. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132967"},{"key":"e_1_3_2_34_2","volume-title":"Spectral Graph Theory","author":"Chung Fan R. K.","year":"1992","unstructured":"Fan R. K. Chung. 1992. Spectral Graph Theory. American Mathematical Society."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220025"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04228D"},{"key":"e_1_3_2_38_2","article-title":"Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning","author":"Mavromatis Costas","year":"2020","unstructured":"Costas Mavromatis and George Karypis. 2020. Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning. arXiv (2020). https:\/\/arxiv.org\/abs\/2009.06946v1.","journal-title":"arXiv"},{"key":"e_1_3_2_39_2","article-title":"Towards sparse hierarchical graph classifiers","author":"Cangea C\u01cet\u01celina","year":"2018","unstructured":"C\u01cet\u01celina Cangea, Petar Veli\u010dkovi\u010d, Nikola Jovanovi\u0107, Thomas N. Kipf, and Pietro Li\u00f2. 2018. Towards sparse hierarchical graph classifiers. arXiv (2018). https:\/\/arxiv.org\/abs\/1811.01287.","journal-title":"arXiv"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.344"},{"key":"e_1_3_2_42_2","article-title":"Relational graph attention networks","author":"Busbridge Dan","year":"2019","unstructured":"Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y. Yhammerla. 2019. Relational graph attention networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1904.05811.","journal-title":"arXiv"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"key":"e_1_3_2_44_2","first-page":"273","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201918)","author":"Beck Daniel","year":"2018","unstructured":"Daniel Beck, Gholamreza Haffari, and Trevor Cohn. 2018. Graph-to-sequence learning using gated graph neural networks. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201918), Vol. 1. ACL, 273\u2013283."},{"key":"e_1_3_2_45_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Selsam Daniel","year":"2019","unstructured":"Daniel Selsam, Matthew Lamm, Benedikt B\u00fcnz, Percy Liang, Leonardo de Moura, and David L. Dill. 2019. Learning a SAT solver from single-bit supervision. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_47_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial attacks on graph neural networks via meta learning. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330905"},{"key":"e_1_3_2_49_2","first-page":"2224","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201915)","author":"Duvenaud David","year":"2015","unstructured":"David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gomez-Bombarelli, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P. Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In The International Conference on Neural Information Processing Systems (NIPS\u201915). Curran Associates, Inc., 2224\u20132232."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.006"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1015"},{"key":"e_1_3_2_55_2","first-page":"4171","volume-title":"The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919)","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919), Vol. 1. ACL, 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2078"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220052"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00396"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2019.06.003"},{"key":"e_1_3_2_61_2","volume-title":"The International Conference on Learning Representations (ICLR\u201915)","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In The International Conference on Learning Representations (ICLR\u201915)."},{"key":"e_1_3_2_62_2","article-title":"Variational recurrent neural networks for graph classification","author":"Pineau Edouard","year":"2019","unstructured":"Edouard Pineau and Nathan de Lara. 2019. Variational recurrent neural networks for graph classification. arXiv (2019). https:\/\/arxiv.org\/abs\/1902.02721.","journal-title":"arXiv"},{"key":"e_1_3_2_63_2","first-page":"10701","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","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. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 10701\u201310711."},{"key":"e_1_3_2_64_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Sun Fan-Yun","year":"2019","unstructured":"Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang. 2019. InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_65_2","article-title":"Explainability techniques for graph convolutional networks","author":"Baldassarre Federico","year":"2019","unstructured":"Federico Baldassarre and Hossein Azizpour. 2019. Explainability techniques for graph convolutional networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1905.13686.","journal-title":"arXiv"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2726981"},{"key":"e_1_3_2_68_2","first-page":"6861","volume-title":"The International Conference on Machine Learning (ICML\u201919)","volume":"97","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Tianyi Zhang, Amauri Holanda de Souza, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying graph convolutional networks. In The International Conference on Machine Learning (ICML\u201919), Vol. 97. PMLR, 6861\u20136871."},{"key":"e_1_3_2_69_2","article-title":"Semi-supervised node classification via hierarchical graph convolutional networks","author":"Hu Fenyu","year":"2019","unstructured":"Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, and Tieniu Tan. 2019. Semi-supervised node classification via hierarchical graph convolutional networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1902.06667v2.","journal-title":"arXiv"},{"key":"e_1_3_2_70_2","article-title":"Multi-scale context aggregation by dilated convolutions","author":"Yu Fisher","year":"2016","unstructured":"Fisher Yu and Vladlen Koltun. 2016. Multi-scale context aggregation by dilated convolutions. arXiv (2016). https:\/\/arxiv.org\/abs\/1511.07122v3.","journal-title":"arXiv"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107000"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2008.2005605"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005141"},{"key":"e_1_3_2_74_2","first-page":"2083","volume-title":"The International Conference on Machine Learning","volume":"97","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph U-Nets. In The International Conference on Machine Learning, Vol. 97. PMLR, 2083\u20132092."},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/195"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_77_2","first-page":"3419","volume-title":"The 37th International Conference on Machine Learning (ICML\u201920)","volume":"119","author":"Garg Vikas","year":"2020","unstructured":"Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola. 2020. Generalization and representational limits of graph neural networks. In The 37th International Conference on Machine Learning (ICML\u201920), Vol. 119. PMLR, 3419\u20133430."},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"e_1_3_2_79_2","first-page":"338","volume-title":"The International Conference on Machine Learning (ICML\u201921)","author":"Li Guohao","year":"2021","unstructured":"Guohao Li, Matthias, Bernard Ghanem, and Vladlen Koltun. 2021. Training graph neural networks with 1000 layers. In The International Conference on Machine Learning (ICML\u201921). PMLR, 338\u2013348. https:\/\/doi.org\/10.1145\/3394486.3403076"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"e_1_3_2_81_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. 2019. Invariant and equivariant graph networks. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_82_2","first-page":"1115","volume-title":"The International Conference on Machine Learning (ICML\u201918)","volume":"80","author":"Dai Hanjun","year":"2018","unstructured":"Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018. Adversarial attack on graph structured data. In The International Conference on Machine Learning (ICML\u201918), Vol. 80. PMLR, 1115\u20131124."},{"key":"e_1_3_2_83_2","article-title":"Iterative graph self-distillation","author":"Zhang Hanlin","year":"2020","unstructured":"Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, and Eric P. Xing. 2020. Iterative graph self-distillation. arXiv (2020). https:\/\/arxiv.org\/abs\/2010.12609v1.","journal-title":"arXiv"},{"key":"e_1_3_2_84_2","article-title":"Explainability in graph neural networks: A taxonomic survey","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2020. Explainability in graph neural networks: A taxonomic survey. arXiv (2020). https:\/\/arxiv.org\/abs\/2012.15445.","journal-title":"arXiv"},{"key":"e_1_3_2_85_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan and Shuiwang Ji. 2020. StructPool: Structured graph pooling via conditional random fields. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_86_2","first-page":"1331","volume-title":"The Annual Meeting of the Association for Computational Linguistics","author":"Zhu Hao","year":"2019","unstructured":"Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. 2019. Graph neural networks with generated parameters for relation extraction. In The Annual Meeting of the Association for Computational Linguistics. ACL, 1331\u20131339."},{"key":"e_1_3_2_87_2","first-page":"4116","volume-title":"The 37th International Conference on Machine Learning","volume":"119","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In The 37th International Conference on Machine Learning, Vol. 119. PMLR, 4116\u20134126."},{"key":"e_1_3_2_88_2","article-title":"Spectral graph attention network","author":"Chang Heng","year":"2020","unstructured":"Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, and Wenwu Zhu. 2020. Spectral graph attention network. arXiv (2020). https:\/\/arxiv.org\/abs\/2003.07450.","journal-title":"arXiv"},{"key":"e_1_3_2_89_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-GCN: Geometric graph convolutional networks. In The International Conference on Learning Representations (ICLR\u201920)."},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330888"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"e_1_3_2_92_2","first-page":"22118","volume-title":"The International Conference on Neural Information Processing Systems","volume":"33","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. In The International Conference on Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 22118\u201322133."},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1488"},{"key":"e_1_3_2_94_2","first-page":"2672","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201914)","volume":"27","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Azron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In The International Conference on Neural Information Processing Systems (NIPS\u201914), Vol. 27. 2672\u20132680."},{"key":"e_1_3_2_95_2","first-page":"551","volume-title":"The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD\u201904)","volume":"32","author":"Dhillon Inderjit S.","year":"2004","unstructured":"Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. 2004. Kernel k-means, spectral clustering and normalized cuts. In The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD\u201904), Vol. 32. ACM, 551\u2013556. https:\/\/doi.org\/10.1145\/1014052.1014118"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1115"},{"key":"e_1_3_2_97_2","first-page":"4868","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"Chami Ines","year":"2019","unstructured":"Ines Chami, Rex Ying, Christopher Re, and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 4868\u20134879."},{"key":"e_1_3_2_98_2","first-page":"1993","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201916)","author":"Atwood James","year":"2016","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural network. In The International Conference on Neural Information Processing Systems (NIPS\u201916). Curran Associates, Inc., 1993\u20132001."},{"key":"e_1_3_2_99_2","article-title":"Towards AI-Complete question answering: A set of prerequisite toy tasks","author":"Weston Jason","year":"2015","unstructured":"Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. 2015. Towards AI-Complete question answering: A set of prerequisite toy tasks. arXiv (2015). https:\/\/arxiv.org\/abs\/1502.05698v1.","journal-title":"arXiv"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"e_1_3_2_101_2","article-title":"Inferring Javascript types using graph neural networks","author":"Schrouff Jessica","year":"2019","unstructured":"Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, and Liam Atkinson. 2019. Inferring Javascript types using graph neural networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1905.06707.","journal-title":"arXiv"},{"key":"e_1_3_2_102_2","article-title":"Topology adaptive graph convolutional networks","author":"Du Jian","year":"2018","unstructured":"Jian Du, Shanghang Zhang, Guanhang Wu, Jos\u00e9 M. F. Moura, and Soummya Kar. 2018. Topology adaptive graph convolutional networks. arXiv (2018). https:\/\/arxiv.org\/abs\/1710.10370.","journal-title":"arXiv"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_3_2_104_2","first-page":"942","volume-title":"The International Conference on Machine Learning (ICML\u201918)","author":"Chen Jianfei","year":"2018","unstructured":"Jianfei Chen, Jun Zhu, and Le Song. 2018. Stochastic training of graph convolutional networks with variance reduction. In The International Conference on Machine Learning (ICML\u201918). PMLR, 942\u2013950."},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00593"},{"key":"e_1_3_2_106_2","first-page":"No. 139","volume-title":"The International Conference on Uncertainty in Artificial Intelligence (UAI\u201918)","author":"Zhang Jiani","year":"2018","unstructured":"Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018. GaAN: Gated attention networks for learning on large and spatiotemporal graphs. In The International Conference on Uncertainty in Artificial Intelligence (UAI\u201918). PMLR, No. 139."},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_41"},{"key":"e_1_3_2_108_2","volume-title":"The International Conference on Data Mining (ICDM\u201921)","author":"Jiao Yizhou","year":"2021","unstructured":"Yizhou Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, and Yangyong Zhu. 2021. Sub-graph contrast for scalable self-supervised graph representation learning. In The International Conference on Data Mining (ICDM\u201921). IEEE."},{"key":"e_1_3_2_109_2","article-title":"Graph-Bert: Only attention is needed for learning graph representations","author":"Zhang Jiawei","year":"2020","unstructured":"Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun. 2020. Graph-Bert: Only attention is needed for learning graph representations. arXiv (2020). https:\/\/arxiv.org\/abs\/2001.05140.","journal-title":"arXiv"},{"key":"e_1_3_2_110_2","first-page":"6412","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"You Jiaxuan","year":"2018","unstructured":"Jiaxuan You, Bowen Liu, Rex Ying, and Vijay Pande. 2018. Graph convolutional policy network for goal-directed molecular graph generation. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Assoicates, Inc., Montreal, Quebec, Canada, 6412\u20136422."},{"key":"e_1_3_2_111_2","first-page":"10552","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201918)","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, and Jure Leskovec. 2019. G2SAT: Learning to generate SAT formulas. In The International Conference on Neural Information Processing Systems (NeurPS\u201918). Curran Associates, Inc., 10552\u201310563."},{"key":"e_1_3_2_112_2","first-page":"5708","volume-title":"The International Conference on Machine Learing (ICML\u201918)","volume":"80","author":"You Jiaxuan","year":"2018","unstructured":"Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. GraphRNN: Generating realistic graphs with deep auto-regressive models. In The International Conference on Machine Learing (ICML\u201918), Vol. 80. PMLR, 5708\u20135717."},{"key":"e_1_3_2_113_2","first-page":"7134","volume-title":"The International Conference on Machine Learning (ICML\u201919)","volume":"97","author":"You Jiaxue","year":"2019","unstructured":"Jiaxue You, Rex Ying, and Jure Leskovec. 2019. Position-aware graph neural networks. In The International Conference on Machine Learning (ICML\u201919), Vol. 97. PMLR, 7134\u20137143."},{"key":"e_1_3_2_114_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Wei Jiayi","year":"2020","unstructured":"Jiayi Wei, Maruth Goyal, Greg Durrett, and Isil Dilling. 2020. LambdaNet: Probabilistic type inference using graph neural networks. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_115_2","volume-title":"The International Conference on Learning Representations (ICLR\u201918)","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 The International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_117_2","first-page":"892","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Zhou Jie","year":"2019","unstructured":"Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2019. GEAR: Graph-based evidence aggregating and reasoning for fact verification. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, 892\u2013901."},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00662"},{"key":"e_1_3_2_121_2","volume-title":"The International Conference on Learning Representations (ICLR\u201914)","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In The International Conference on Learning Representations (ICLR\u201914)."},{"key":"e_1_3_2_122_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict the propagate: Graph neural networks meet personalized pagerank. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363574"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219980"},{"key":"e_1_3_2_125_2","first-page":"282","volume-title":"The International Conference on Machine Learning (ICML\u201901)","volume":"97","author":"Lafferty John","year":"2001","unstructured":"John Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random field: Probabilistic models for segmenting and labeling sequence data. In The International Conference on Machine Learning (ICML\u201901), Vol. 97. PMLR, 282\u2013289."},{"key":"e_1_3_2_126_2","volume-title":"the IEEE International Conference on Data Mining (ICDM)","author":"Li Juanhui","year":"2020","unstructured":"Juanhui Li, Yao Ma, Yiqi Wang, Charu Aggarwal, Chang-Dong Wang, and Jiliang Tang. 2020. Graph pooling with representativeness. In the IEEE International Conference on Data Mining (ICDM). IEEE, 20424165. https:\/\/doi.org\/10.1109\/ICDM50108.2020.00039"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330950"},{"key":"e_1_3_2_128_2","first-page":"3734","volume-title":"The International Conference on Machine Learning (ICML\u201919)","volume":"97","author":"Lee Junhyun","year":"2019","unstructured":"Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019. Self-attention graph pooling. In The International Conference on Machine Learning (ICML\u201919), Vol. 97. PMLR, 3734\u20133743."},{"key":"e_1_3_2_129_2","first-page":"1263","volume-title":"The International Conference on Machine Learning (ICML\u201917)","volume":"70","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 The International Conference on Machine Learning (ICML\u201917), Vol. 70. PMLR, 1263\u20131272."},{"key":"e_1_3_2_130_2","first-page":"1556","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201915)","author":"Tai Kai Sheng","year":"2015","unstructured":"Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201915), Vol. 1. ACL, 1556\u20131566."},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_132_2","article-title":"Auto-GNN: Neural architecture search of graph neural networks","author":"Zhou Kaixiong","year":"2019","unstructured":"Kaixiong Zhou, Qingquan Song, Xiao Huang, and Xia Hu. 2019. Auto-GNN: Neural architecture search of graph neural networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1909.03184.","journal-title":"arXiv"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000076"},{"key":"e_1_3_2_134_2","first-page":"No. 161","volume-title":"The International Conference on Uncertainty in Artificial Intelligence (UAI\u201919)","author":"Sun Ke","year":"2019","unstructured":"Ke Sun, Piotr Koniusz, and Zhen Wang. 2019. Fisher-Bures adversary graph convolutional networks. In The International Conference on Uncertainty in Artificial Intelligence (UAI\u201919). PMLR, No. 161."},{"key":"e_1_3_2_135_2","first-page":"426","volume-title":"The International Conference on Artificial Intelligence (AAAI\u201918)","author":"Tu Ke","year":"2018","unstructured":"Ke Tu, Peng Cui, Xiao Wang, Fei Wang, and Wenwu Zhu. 2018. Structural deep embedding for hyper-networks. In The International Conference on Artificial Intelligence (AAAI\u201918). Association for the Advances of Artificial Intelligence, 426\u2013433."},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220068"},{"key":"e_1_3_2_137_2","first-page":"5453","volume-title":"The International Conference on Machine Learning (ICML\u201918)","volume":"80","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 The International Conference on Machine Learning (ICML\u201918), Vol. 80. PMLR, 5453\u20135462."},{"key":"e_1_3_2_138_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Xu Keyulu","year":"2020","unstructured":"Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2020. What can neural networks reason about. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_139_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_140_2","volume-title":"The International Conference on Learning Representations (ICLR\u201918)","author":"Yoon KiJung","year":"2018","unstructured":"KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, and Xaq Pitkow. 2018. Inference in probabilistic graphical models by graph neural networks. In The International Conference on Learning Representations (ICLR\u201918). Vancouver, Canada."},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553516"},{"key":"e_1_3_2_142_2","article-title":"Attention-based graph neural network for semi-supervised learning","author":"Thekumparampil Kiran K.","year":"2018","unstructured":"Kiran K. Thekumparampil, Chong Wang, Sewoong Oh, and Lijia Li. 2018. Attention-based graph neural network for semi-supervised learning. arXiv (2018). https:\/\/arxiv.org\/abs\/1803.03735.","journal-title":"arXiv"},{"key":"e_1_3_2_143_2","article-title":"Graph2Seq: Graph to sequence learning with attention-based neural networks","author":"Xu Kun","year":"2018","unstructured":"Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, and Vadim Sheinin. 2018. Graph2Seq: Graph to sequence learning with attention-based neural networks. arXiv (2018). https:\/\/arxiv.org\/abs\/1804.00823.","journal-title":"arXiv"},{"key":"e_1_3_2_144_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_145_2","first-page":"4465","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201920)","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. In The International Conference on Neural Information Processing Systems (NeurPS\u201920), Vol. 33. Curran Associates, Inc., 4465\u20134478."},{"key":"e_1_3_2_146_2","article-title":"Adversarial attack and defense on graph data: A survey","author":"Sun Lichao","year":"2020","unstructured":"Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, and Bo Li. 2020. Adversarial attack and defense on graph data: A survey. arXiv (2020). https:\/\/arxiv.org\/abs\/1812.10528.","journal-title":"arXiv"},{"key":"e_1_3_2_147_2","first-page":"6140","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Qiu Lin","year":"2019","unstructured":"Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019. Dynamically fused graph network for multi-hop reasoning. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, 6140\u20136150."},{"key":"e_1_3_2_148_2","first-page":"2291","volume-title":"The International Conference on Artificial Intelligence and Statistics (AISTATS\u201919)","volume":"89","author":"Liu Linfeng","year":"2019","unstructured":"Linfeng Liu and Liping Liu. 2019. Amortized variational inference with graph convolutional networks for gaussian processes. In The International Conference on Artificial Intelligence and Statistics (AISTATS\u201919), Vol. 89. Society for Artificial Intelligence and Statistics, 2291\u20132300."},{"key":"e_1_3_2_149_2","article-title":"Towards efficient large-scale graph neural network computing","author":"Ma Lingxiao","year":"2018","unstructured":"Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai. 2018. Towards efficient large-scale graph neural network computing. arXiv (2018). https:\/\/arxiv.org\/abs\/1810.08403.","journal-title":"arXiv"},{"key":"e_1_3_2_150_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Zhao Lingxiao","year":"2020","unstructured":"Lingxiao Zhao and Leman Akoglu. 2020. PairNorm: Tackling oversmoothing in GNNs. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01041"},{"key":"e_1_3_2_152_2","first-page":"7440","volume-title":"The International Conference on Computer Vision and Pattern Recognition (CVPR\u201919)","author":"Landrieu Loic","year":"2019","unstructured":"Loic Landrieu and Mohamed Boussaha. 2019. Point Cloud over Segmentation with graph-structured deep metric learning. In The International Conference on Computer Vision and Pattern Recognition (CVPR\u201919). IEEE, 7440\u20137449."},{"key":"e_1_3_2_153_2","article-title":"Variational spectral graph convolutional networks","author":"Tiao Louis","year":"2019","unstructured":"Louis Tiao, Pantelis Elinas, Harrison Nguyen, and Edwin V. Bonilla. 2019. Variational spectral graph convolutional networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1906.01852v1.","journal-title":"arXiv"},{"key":"e_1_3_2_154_2","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2019.8902995"},{"key":"e_1_3_2_155_2","first-page":"4731","volume-title":"The Conference on Artificial Intelligence (AAAI\u201919)","author":"Prates Marcelo","year":"2019","unstructured":"Marcelo Prates, Pedro H. C. Avelar, Henrique Lemos, Luis C. Lamb, and Moshe Y. Vardi. 2019. Learing to solve NP-Complete problems: A graph neural network for decision TSP. In The Conference on Artificial Intelligence (AAAI\u201919). Association for the Advances of Artificial Intelligence, 4731\u20134738."},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"},{"key":"e_1_3_2_157_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.11"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"e_1_3_2_159_2","first-page":"2014","volume-title":"The International Conference on Machine Learning (ICLR\u201916)","volume":"48","author":"Niepert Mathias","year":"2016","unstructured":"Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In The International Conference on Machine Learning (ICLR\u201916), Vol. 48. 2014\u20132023."},{"key":"e_1_3_2_160_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_161_2","first-page":"2654","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201918)","author":"Narasimhan Medhini","year":"2018","unstructured":"Medhini Narasimhan, Svetlana Lazebnik, and Alexander G. Schwing. 2018. Out of the box: Reasoning with graph convolution nets for factual visual question answering. In The International Conference on Neural Information Processing Systems (NIPS\u201918). Curran Associates, Inc., 2654\u20132665."},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"},{"key":"e_1_3_2_163_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_164_2","first-page":"5241","volume-title":"The International Conference on Machine Learning (ICML\u201919)","volume":"97","author":"Qu Meng","year":"2019","unstructured":"Meng Qu, Yoshua Bengio, and Jian Tang. 2019. GMNN: Graph markov neural networks. In The International Conference on Machine Learning (ICML\u201919), Vol. 97. PMLR, 5241\u20135250."},{"key":"e_1_3_2_165_2","first-page":"2220","volume-title":"The International Conference Neural Information Processing Systems (NeurPS\u201920)","volume":"33","author":"Mesquita Diego","year":"2020","unstructured":"Diego Mesquita, Amauri H. Souza, and Samuel Kaski. 2020. Rethinking pooling in graph neural networks. In The International Conference Neural Information Processing Systems (NeurPS\u201920), Vol. 33. Curran Associates, Inc., 2220\u20132231."},{"key":"e_1_3_2_166_2","first-page":"3844","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201916)","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In The International Conference on Neural Information Processing Systems (NIPS\u201916), Curran Associates, Inc.3844\u20133852."},{"key":"e_1_3_2_167_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_3_2_168_2","article-title":"Deep convolutional networks on graph-structured data","author":"Henaff Mikael","year":"2015","unstructured":"Mikael Henaff, Joan Bruna, and Yan LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv (2015). https:\/\/arxiv.org\/abs\/1506.05163.","journal-title":"arXiv"},{"key":"e_1_3_2_169_2","volume-title":"The International Conference on Learning Representations (ICLR\u201918)","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to represent programs with graphs. In The International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_3_2_170_2","first-page":"2694","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Ding Ming","year":"2019","unstructured":"Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019. Cognitive graph for multi-hop reading comprehension at scale. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, 2694\u20132703."},{"key":"e_1_3_2_171_2","first-page":"2704","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Tu Ming","year":"2019","unstructured":"Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, and Bowen Zhou. 2019. Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, 2704\u20132713."},{"key":"e_1_3_2_172_2","volume-title":"The International Conference on Learning Representations (ICLR Workshop\u201919)","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander Smola, and Zheng Zhang. 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. In The International Conference on Learning Representations (ICLR Workshop\u201919)."},{"key":"e_1_3_2_173_2","first-page":"5171","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201918)","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In The International Conference on Neural Information Processing Systems (NIPS\u201918). Curran Associates, Inc., 5171\u20135181."},{"key":"e_1_3_2_174_2","first-page":"4438","volume-title":"The Conference on Artificial Intelligence (AAAI\u201918)","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 The Conference on Artificial Intelligence (AAAI\u201918). Association for the Advances of Artificial Intelligence, 4438\u20134445."},{"key":"e_1_3_2_175_2","first-page":"1511","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"Yadati Naganand","year":"2019","unstructured":"Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. 2019. HyperGCN: A new method for training graph convolutional networks on hypergraphs. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 1511\u20131522."},{"key":"e_1_3_2_176_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75762-5_36"},{"key":"e_1_3_2_177_2","first-page":"4539","volume-title":"The International Conference on Neural Inforamtion Processing Systems (NIPS\u201917)","author":"Watters Nicholas","year":"2017","unstructured":"Nicholas Watters, Andrea Tacchetti, Th\u00e9ophane Weber, Razvan Pascanu, Peter Battaglia, and Daniel Zoran. 2017. Visual interaction networks: Learning a physics simulator from video. In The International Conference on Neural Inforamtion Processing Systems (NIPS\u201917). Curran Associates, Inc., 4539\u20134547."},{"key":"e_1_3_2_178_2","article-title":"MolGAN: An implicit generative model for small molecular graphs","author":"Cao Nicola De","year":"2018","unstructured":"Nicola De Cao and Thomas N. Kipf. 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv (2018). https:\/\/arxiv.org\/abs\/1805.11973.","journal-title":"arXiv"},{"key":"e_1_3_2_179_2","first-page":"2306","volume-title":"The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919)","author":"Cao Nicola De","year":"2019","unstructured":"Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019. Question answering by reasoning across documents with graph convolutional networks. In The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919). ACL, 2306\u20132317."},{"key":"e_1_3_2_180_2","first-page":"7092","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"Keriven Nicolas","year":"2019","unstructured":"Nicolas Keriven and Gabriel Peyr\u00e9. 2019. Universal invariant and equivariant graph neural networks. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 7092\u20137101."},{"key":"e_1_3_2_181_2","article-title":"Pre-training graph neural networks with kernels","author":"Navarin Nicol\u00f3","year":"2018","unstructured":"Nicol\u00f3 Navarin, Dinh V. Tran, and Alessandro Sperduti. 2018. Pre-training graph neural networks with kernels. arXiv (2018). https:\/\/arxiv.org\/abs\/1811.06930v1.","journal-title":"arXiv"},{"key":"e_1_3_2_182_2","first-page":"15413","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"Dehmamy Nima","year":"2019","unstructured":"Nima Dehmamy, Albert-Laszlo Barabasi, and Rose Yu. 2019. Understanding the representation power of graph neural networks in learning graph topology. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 15413\u201315423."},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1306"},{"key":"e_1_3_2_184_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_185_2","article-title":"Pitfalls of graph neural network evaluation","author":"Shchur Oleksandr","year":"2019","unstructured":"Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Pitfalls of graph neural network evaluation. arXiv (2019). https:\/\/arxiv.org\/abs\/1811.05868.","journal-title":"arXiv"},{"key":"e_1_3_2_186_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01363"},{"key":"e_1_3_2_187_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Barcel\u00f3 Pablo","year":"2020","unstructured":"Pablo Barcel\u00f3, Egor V. Kostylev, Mikael Monet, Jorge P\u00e9rez, Juan Reutter, and Juan Pablo Silva. 2020. The logical expressiveness of graph neural networks. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_188_2","article-title":"Function space pooling for graph convolutional networks","author":"Corcoran Padraig","year":"2019","unstructured":"Padraig Corcoran. 2019. Function space pooling for graph convolutional networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1905.06259.","journal-title":"arXiv"},{"key":"e_1_3_2_189_2","doi-asserted-by":"publisher","DOI":"10.1109\/43.310898"},{"key":"e_1_3_2_190_2","article-title":"Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case","author":"Almasan Paul","year":"2019","unstructured":"Paul Almasan, Jos\u00e9 Su\u00e1rez-Varela, Arnau Badia-Sampera, Krzysztof Rusek, Pere Barlet-Ros, and Albert Cabellos-Aparicio. 2019. Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case. arXiv (2019). https:\/\/arxiv.org\/abs\/1910.07421.","journal-title":"arXiv"},{"key":"e_1_3_2_191_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330912"},{"key":"e_1_3_2_192_2","volume-title":"The International Conference on Learning Representations (ICLR\u201918)","author":"Veli\u010dkovi\u010d Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u010d, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In The International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_3_2_193_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Veli\u010dkovi\u010d Petar","year":"2019","unstructured":"Petar Veli\u010dkovi\u010d, William Fedus, William L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep graph infomax. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_194_2","first-page":"4502","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201916)","author":"Battaglia Peter","year":"2016","unstructured":"Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu. 2016. Interaction networks for learning about objects, relations and physics. In The International Conference on Neural Information Processing Systems (NIPS\u201916). Curran Associates, Inc., 4502\u20134510."},{"key":"e_1_3_2_195_2","article-title":"PiNet: A permutation invariant graph neural network for graph classification","author":"Meltzer Peter","year":"2019","unstructured":"Peter Meltzer, Marcelo Daniel Gutierrez Mallea, and Peter J. Bentley. 2019. PiNet: A permutation invariant graph neural network for graph classification. arXiv (2019). https:\/\/arxiv.org\/abs\/1905.03046.","journal-title":"arXiv"},{"key":"e_1_3_2_196_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, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. 2018. Relational inductive biases, deep learning, and graph networks. arXiv (2018). https:\/\/arxiv.org\/abs\/1806.01261.","journal-title":"arXiv"},{"key":"e_1_3_2_197_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01103"},{"key":"e_1_3_2_198_2","first-page":"3538","volume-title":"The Conference on Artificial Intelligence (AAAI\u201918)","author":"Li Qimai","year":"2018","unstructured":"Qimai Li, Zhichao Han, and Xiaoming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In The Conference on Artificial Intelligence (AAAI\u201918). Association for the Advances of Artificial Intelligence, 3538\u20133545."},{"key":"e_1_3_2_199_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-5014-9_12"},{"key":"e_1_3_2_200_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6964"},{"key":"e_1_3_2_201_2","volume-title":"The International Conference on Learning Representations Workshop","author":"Liao Renjie","year":"2018","unstructured":"Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, and Richard Zemel. 2018. Graph partition neural networks for semi-supervised classification. In The International Conference on Learning Representations Workshop."},{"key":"e_1_3_2_202_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Liao Renjie","year":"2019","unstructured":"Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard Zemel. 2019. LanczosNet: Multi-scale deep graph convolutional networks. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_203_2","first-page":"4805","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201918)","author":"Ying Rex","year":"2018","unstructured":"Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In The International Conference on Neural Information Processing Systems (NeurPS\u201918). Curran Associates, Inc., 4805\u20134815."},{"key":"e_1_3_2_204_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2316836"},{"key":"e_1_3_2_205_2","first-page":"2284","volume-title":"The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919)","volume":"1","author":"Koncel-Kedziorski Rik","year":"2019","unstructured":"Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019. Text generation from knowledge graphs with graph transformers. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919), Vol. 1. ACL, 2284\u20132293. https:\/\/doi.org\/10.18653\/v1\/N19-1238"},{"key":"e_1_3_2_206_2","volume-title":"The International Conference on Learning Representations Workshop","author":"Kondor Risi","year":"2018","unstructured":"Risi Kondor, Truong Son Hy, Horace Pan, Brandon M. Anderson, and Shubhendu Trivedi. 2018. Covariant compositional networks for learning graphs. In The International Conference on Learning Representations Workshop."},{"key":"e_1_3_2_207_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2879624"},{"key":"e_1_3_2_208_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403358"},{"key":"e_1_3_2_209_2","first-page":"3546","volume-title":"The Conference on Artificial Intelligence (AAAI\u201918)","author":"Li Ruoyu","year":"2018","unstructured":"Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018. Adaptive graph convolutional neural networks. In The Conference on Artificial Intelligence (AAAI\u201918). Associations for the Advances of Artificial Intelligence, 3546\u20133553. https:\/\/doi.org\/10.1145\/3292500.3330925"},{"key":"e_1_3_2_210_2","first-page":"4663","volume-title":"The International Conference on Machine Learning (ICML\u201919)","volume":"97","author":"Murphy Ryan","year":"2019","unstructured":"Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro. 2019. Relational pooling for graph representations. In The International Conference on Machine Learning (ICML\u201919), Vol. 97. PMLR, 4663\u20134673."},{"key":"e_1_3_2_211_2","article-title":"A survey on the expressive power of graph neural networks","author":"Sato Ryoma","year":"2020","unstructured":"Ryoma Sato. 2020. A survey on the expressive power of graph neural networks. arXiv (2020). https:\/\/arxiv.org\/abs\/2003.04078.","journal-title":"arXiv"},{"key":"e_1_3_2_212_2","first-page":"4081","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"Sato Ryoma","year":"2019","unstructured":"Ryoma Sato, Makoto Yamada, and Hisashi Kashima. 2019. Approximation ratios of graph neural networks for combinatorial problems. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 4081\u20134090."},{"key":"e_1_3_2_213_2","first-page":"No. 310","volume-title":"The Conference on Uncertainty in Artificial Intelligence (UAI\u201919)","author":"Abu-EL-Haija Sami","year":"2019","unstructured":"Sami Abu-EL-Haija, Amol Kapoor, Bryan Perozzi, and Joonseok Lee. 2019. N-GCN: Multi-scale graph convolution for semi-supervised node classification. In The Conference on Uncertainty in Artificial Intelligence (UAI\u201919). PMLR, No. 310."},{"key":"e_1_3_2_214_2","first-page":"21","volume-title":"The International Conference on Machine Learning (ICML\u201919)","volume":"97","author":"Abu-EL-Haija Sami","year":"2019","unstructured":"Sami Abu-EL-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In The International Conference on Machine Learning (ICML\u201919), Vol. 97. PMLR, 21\u201329."},{"key":"e_1_3_2_215_2","first-page":"3856","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201917)","volume":"30","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. In The International Conference on Neural Information Processing Systems (NIPS\u201917), Vol. 30. Curran Associates, Inc., 3856\u20133866."},{"key":"e_1_3_2_216_2","article-title":"Graph capsule convolutional neural networks","author":"Verma Saurabh","year":"2018","unstructured":"Saurabh Verma and Zhili Zhang. 2018. Graph capsule convolutional neural networks. arXiv (2018). https:\/\/arxiv.org\/abs\/1805.08090.","journal-title":"arXiv"},{"key":"e_1_3_2_217_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0237-x"},{"key":"e_1_3_2_218_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_219_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_220_2","first-page":"1145","volume-title":"The Conference on Artificial Intelligence (AAAI\u201916)","author":"Cao Shaosheng","year":"2016","unstructured":"Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In The Conference on Artificial Intelligence (AAAI\u201916). Association for the Advances of Artificial Intelligence, 1145\u20131152."},{"key":"e_1_3_2_221_2","article-title":"Transfer active learning for graph neural networks","author":"Hu Shengding","year":"2020","unstructured":"Shengding Hu, Meng Qu, Zhiyuan Liu, and Jian Tang. 2020. Transfer active learning for graph neural networks. OpenReview (2020). https:\/\/openreview.net\/forum?id=BklOXeBFDS.","journal-title":"OpenReview"},{"key":"e_1_3_2_222_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_223_2","article-title":"Motif-driven contrastive learning of graph representations","author":"Zhang Shichang","year":"2021","unstructured":"Shichang Zhang, Ziniu Hu, Arjun Subramonian, and Yizhou Sun. 2021. Motif-driven contrastive learning of graph representations. arXiv (2021). https:\/\/arxiv.org\/abs\/2012.12533v2.","journal-title":"arXiv"},{"key":"e_1_3_2_224_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"e_1_3_2_225_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00998"},{"key":"e_1_3_2_226_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_227_2","first-page":"7444","volume-title":"The Conference on Artificial Intelligence (AAAI\u201918)","author":"Yan Sijie","year":"2018","unstructured":"Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In The Conference on Artificial Intelligence (AAAI\u201918). Association for the Advances of Artificial Intelligence, 7444\u20137452."},{"key":"e_1_3_2_228_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66182-7_54"},{"key":"e_1_3_2_229_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6730"},{"key":"e_1_3_2_230_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"e_1_3_2_231_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/490"},{"key":"e_1_3_2_232_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1096"},{"key":"e_1_3_2_233_2","article-title":"CGNF: Conditional graph neural fields","author":"Ma Tengfei","year":"2019","unstructured":"Tengfei Ma, Junyuan Shang, and Jimeng Sun. 2019. CGNF: Conditional graph neural fields. OpenReview (2019). https:\/\/openreview.net\/forum?id=ryxMX2R9YQ.","journal-title":"OpenReview"},{"key":"e_1_3_2_234_2","first-page":"5900","volume-title":"The Conference on Artificial Intelligence (AAAI\u201918)","author":"Nguyen Thien Huu","year":"2018","unstructured":"Thien Huu Nguyen and Ralph Grishman. 2018. Graph convolutional networks with argument-aware pooling for event detection. In The Conference on Artificial Intelligence (AAAI\u201918). Association for the Advances of Artificial Intelligence, 5900\u20135907."},{"key":"e_1_3_2_235_2","first-page":"1","article-title":"Neural architecture search: A survey","volume":"20","author":"Elsken Thomas","year":"2019","unstructured":"Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019. Neural architecture search: A survey. Journal of Machine Learning Research 20 (2019), 1\u201321. https:\/\/doi.org\/10.1002\/j.1538-7305.1970.tb01770.x","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_236_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Kipf Thomas N.","year":"2020","unstructured":"Thomas N. Kipf, Elise van der Pol, and Max Welling. 2020. Contrastive learning of structured world models. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_237_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 (2016). https:\/\/arxiv.org\/abs\/1611.07308.","journal-title":"arXiv"},{"key":"e_1_3_2_238_2","volume-title":"The International Conference on Learning Representations (ICLR\u201917)","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In The International Conference on Learning Representations (ICLR\u201917)."},{"key":"e_1_3_2_239_2","article-title":"Spectral pyramid graph attention network for hyperspectral image classification","author":"Wang Tinghuai","year":"2020","unstructured":"Tinghuai Wang, Guangming Wang, Kuan Eeik Tan, and Donghui Tan. 2020. Spectral pyramid graph attention network for hyperspectral image classification. arXiv (2020). https:\/\/arxiv.org\/abs\/2001.07108.","journal-title":"arXiv"},{"key":"e_1_3_2_240_2","volume-title":"The International Conference on Learning Representations (ICLR\u201918)","author":"Wang Tingwu","year":"2018","unstructured":"Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler. 2018. NerveNet: Learning structured policy with graph neural networks. In The International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_3_2_241_2","first-page":"2485","volume-title":"The Conference on Artificial Intelligence (AAAI\u201917)","author":"Pham Trang","year":"2017","unstructured":"Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. 2017. Column networks for collective classification. In The Conference on Artificial Intelligence (AAAI\u201917). Association for the Advances of Artificial Intelligence, 2485\u20132491."},{"key":"e_1_3_2_242_2","first-page":"1409","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Fu Tsu-Jui","year":"2019","unstructured":"Tsu-Jui Fu, Peng-Hsuan Li, and Wei-Yun Ma. 2019. GraphRel: Modeling text as relational graphs for joint entity and relation extraction. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, 1409\u20131418."},{"key":"e_1_3_2_243_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00113"},{"key":"e_1_3_2_244_2","article-title":"On the bottleneck of graph neural networks and its practical implications","author":"Alon Uri","year":"2020","unstructured":"Uri Alon and Eran Yahav. 2020. On the bottleneck of graph neural networks and its practical implications. arXiv (2020). https:\/\/arxiv.org\/abs\/2006.05205v2.","journal-title":"arXiv"},{"key":"e_1_3_2_245_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00009"},{"key":"e_1_3_2_246_2","article-title":"Benchmarking graph neural networks","author":"Dwivedi Vijay Prakash","year":"2020","unstructured":"Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2020. Benchmarking graph neural networks. arXiv (2020). https:\/\/arxiv.org\/abs\/2003.00982.","journal-title":"arXiv"},{"key":"e_1_3_2_247_2","unstructured":"Juho Kannala and Jian Tang. 2019. GraphMix: Regularized training of graph neural networks for semi-supervised learning. arXiv(2020) https:\/\/arxiv.org\/abs\/1909.11715v1."},{"key":"e_1_3_2_248_2","first-page":"778","volume-title":"The International Joint Conference on Neural Networks (IJCNN\u201906)","author":"Massa Vincenzo Di","year":"2006","unstructured":"Vincenzo Di Massa, Cabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, and Marco Gori. 2006. A comparison between recursive neural networks and graph neural networks. In The International Joint Conference on Neural Networks (IJCNN\u201906). IEEE, 778\u2013785. https:\/\/doi.org\/10.1109\/IJCNN.2006.246763"},{"key":"e_1_3_2_249_2","first-page":"2204","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201914)","volume":"2","author":"Minh Volodymyr","year":"2014","unstructured":"Volodymyr Minh, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. 2014. Recurrent models of visual attention. In The International Conference on Neural Information Processing Systems (NIPS\u201914), Vol. 2. Curran Associates, Inc., 2204\u20132212."},{"key":"e_1_3_2_250_2","first-page":"4843","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Li Wei","year":"2019","unstructured":"Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, and Xu Sun. 2019. Coherent comments generation for Chinese articles with a graph-to-sequence model. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, Florence, Italy, 4843\u20134852."},{"key":"e_1_3_2_251_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.9"},{"key":"e_1_3_2_252_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec. 2020. Strategies for pre-training graph neural networks. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_253_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_254_2","first-page":"4563","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201918)","author":"Huang Wenbing","year":"2018","unstructured":"Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018. Adaptive sampling towards fast graph representation learning. In The International Conference on Neural Information Processing Systems (NeurPS\u201918). Curran Associates, Inc., 4563\u20134572."},{"key":"e_1_3_2_255_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220000"},{"key":"e_1_3_2_256_2","first-page":"2323","volume-title":"The International Conference on Machine Learning (ICML\u201918)","volume":"80","author":"Jin Wengong","year":"2018","unstructured":"Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction tree variational autoencoder for molecular graph generation. In The International Conference on Machine Learning (ICML\u201918), Vol. 80. PMLR, 2323\u20132332."},{"key":"e_1_3_2_257_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31756-0_6"},{"key":"e_1_3_2_258_2","first-page":"1024","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201917)","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In The International Conference on Neural Information Processing Systems (NIPS\u201917). Curran Associates, Inc., 1024\u20131034."},{"key":"e_1_3_2_259_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Kool Wouter","year":"2019","unstructured":"Wouter Kool, Herke van Hoof, and Max Welling. 2019. Attention, learn to solve routing problems. In The International Conference on Learning Representations (ICLR\u201919). https:\/\/openreview.net\/forum?id=ByxBFsRqYm."},{"key":"e_1_3_2_260_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_261_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330941"},{"key":"e_1_3_2_262_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1156"},{"key":"e_1_3_2_263_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2871503"},{"key":"e_1_3_2_264_2","first-page":"2022","volume-title":"The World Wide Web Conference (WWW\u201918)","author":"Wang Xiao","year":"2018","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Philip S. Yu, and Yanfang Ye. 2018. Heterogeneous graph attention network. In The World Wide Web Conference (WWW\u201918). 2022\u20132032. https:\/\/doi.org\/10.1145\/3308558.3313562"},{"key":"e_1_3_2_265_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"e_1_3_2_266_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.234"},{"key":"e_1_3_2_267_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_8"},{"key":"e_1_3_2_268_2","article-title":"Deep graph translation","author":"Guo Xiaojie","year":"2018","unstructured":"Xiaojie Guo, Lingfei Wu, and Liang Zhao. 2018. Deep graph translation. arXiv (2018). https:\/\/arxiv.org\/abs\/1805.09980.","journal-title":"arXiv"},{"key":"e_1_3_2_269_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_3_2_270_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Xu Xiaoran","year":"2020","unstructured":"Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, and Zhihong Zhang. 2020. Dynamically pruned message passing networks for large-scale knowledge graph reasoning. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_271_2","article-title":"Tensor graph convolutional networks for text classification","author":"Liu Xien","year":"2020","unstructured":"Xien Liu, Xinxin You, Xiao Zhang, Ji Wu, and Ping Lv. 2020. Tensor graph convolutional networks for text classification. arXiv (2020). https:\/\/arxiv.org\/abs\/2001.05313.","journal-title":"arXiv"},{"key":"e_1_3_2_272_2","article-title":"Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks","author":"Jiang Xin","year":"2019","unstructured":"Xin Jiang, Kewei Cheng, Song Jiang, and Yizhou Sun. 2019. Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks. OpenReview (2019). https:\/\/openreview.net\/forum?id=rJl05AVtwB.","journal-title":"OpenReview"},{"key":"e_1_3_2_273_2","volume-title":"The International Conference on Learning Representations (ICLR\u201919)","author":"Zhang Xinyi","year":"2019","unstructured":"Xinyi Zhang and Lihui Chen. 2019. Capsule graph neural network. In The International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_274_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11431-020-1647-3"},{"key":"e_1_3_2_275_2","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","volume":"32","author":"Yang Carl","year":"2019","unstructured":"Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, and Pan Li. 2019. Conditional structure generation through graph variational generative adversarial nets. In The International Conference on Neural Information Processing Systems (NeurPS\u201919), Vol. 32. Curran Associates, Inc."},{"key":"e_1_3_2_276_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_277_2","article-title":"CAGNN: Cluster-aware graph neural networks for unsupervised graph representation learning","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Shu Wu, and Liang Wang. 2020. CAGNN: Cluster-aware graph neural networks for unsupervised graph representation learning. arXiv (2020). https:\/\/arxiv.org\/abs\/2009.01674.","journal-title":"arXiv"},{"key":"e_1_3_2_278_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.74"},{"key":"e_1_3_2_279_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330982"},{"key":"e_1_3_2_280_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401092"},{"key":"e_1_3_2_281_2","article-title":"Self-supervised learning of graph neural networks: A unified review","author":"Xie Yaochen","year":"2021","unstructured":"Yaochen Xie, Zhao Xu, Zhengyang Wang, and Shuiwang Ji. 2021. Self-supervised learning of graph neural networks: A unified review. arXiv (2021). https:\/\/arxiv.org\/abs\/2102.10757v2.","journal-title":"arXiv"},{"key":"e_1_3_2_282_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107451"},{"key":"e_1_3_2_283_2","first-page":"2698","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201917)","author":"Hoshen Yedid","year":"2017","unstructured":"Yedid Hoshen. 2017. VAIN: Attentional multi-agent predictive modeling. In The International Conference on Neural Information Processing Systems (NIPS\u201917). Curran Associates, Inc., 2698\u20132708."},{"key":"e_1_3_2_284_2","first-page":"6786","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201918)","author":"Shen Yelong","year":"2018","unstructured":"Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao. 2018. M-Walk: Learning to walk over graphs using monte carlo tree search. In The International Conference on Neural Information Processing Systems (NIPS\u201918). Curran Associates, Inc., 6786\u20136797."},{"key":"e_1_3_2_285_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_286_2","article-title":"HNHN: Hypergraph networks with hyperedge neurons","author":"Dong Yihe","year":"2020","unstructured":"Yihe Dong, Will Sawin, and Yoshua Bengio. 2020. HNHN: Hypergraph networks with hyperedge neurons. arXiv (2020). https:\/\/arxiv.org\/abs\/2006.12278.","journal-title":"arXiv"},{"key":"e_1_3_2_287_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_21"},{"key":"e_1_3_2_288_2","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351034"},{"key":"e_1_3_2_289_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/181"},{"key":"e_1_3_2_290_2","article-title":"Graph self-supervised learning: A survey","author":"Liu Yixin","year":"2021","unstructured":"Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, and Philip S. Yu. 2021. Graph self-supervised learning: A survey. arXiv (2021). https:\/\/arxiv.org\/abs\/2103.00111v1.","journal-title":"arXiv"},{"key":"e_1_3_2_291_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186106"},{"key":"e_1_3_2_292_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6833"},{"key":"e_1_3_2_293_2","first-page":"5812","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201920)","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In The International Conference on Neural Information Processing Systems (NeurPS\u201920), Vol. 33. Curran Associates, Inc., 5812\u20135823."},{"key":"e_1_3_2_294_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"e_1_3_2_295_2","first-page":"357","volume-title":"The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919)","author":"Cao Yu","year":"2019","unstructured":"Yu Cao, Meng Fang, and Dacheng Tao. 2019. BAG: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201919). ACL, 357\u2013362."},{"key":"e_1_3_2_296_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Chen Yu","year":"2020","unstructured":"Yu Chen, Lingfei Wu, and Mohammed J. Zaki. 2020. Reinforcement learning based graph-to-sequence model for natural question generation. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_297_2","article-title":"Learning graph-level representations with recurrent neural networks","author":"Jin Yu","year":"2018","unstructured":"Yu Jin and Joseph F. JaJa. 2018. Learning graph-level representations with recurrent neural networks. arXiv (2018). https:\/\/arxiv.org\/abs\/1805.07683.","journal-title":"arXiv"},{"key":"e_1_3_2_298_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards deep graph convolutional networks on node classification. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_299_2","doi-asserted-by":"publisher","DOI":"10.1145\/3364226"},{"key":"e_1_3_2_300_2","article-title":"Graph transformer","author":"Li Yuan","year":"2019","unstructured":"Yuan Li, Xiaodan Liang, Zhiting Hu, Yinbo Chen, and Eric P. Xing. 2019. Graph transformer. OpenReview (2019). https:\/\/openreview.net\/forum?id=HJei-2RcK7.","journal-title":"OpenReview"},{"key":"e_1_3_2_301_2","volume-title":"The International Conference on Artificial Intelligence (AAAI\u201921)","author":"Lu Yuanfu","year":"2021","unstructured":"Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi. 2021. Learning to pre-train graph neural networks. In The International Conference on Artificial Intelligence (AAAI\u201921). Association for the Advances of Artificial Intelligence."},{"key":"e_1_3_2_302_2","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"e_1_3_2_303_2","first-page":"317","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201918)","author":"Zhang Yue","year":"2018","unstructured":"Yue Zhang, Qi Liu, and Linfeng Song. 2018. Sentence-State LSTM for text representation. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201918), Vol. 1. ACL, 317\u2013327. https:\/\/doi.org\/10.18653\/v1\/P18-1030"},{"key":"e_1_3_2_304_2","volume-title":"The International Conference on Learning Representations (ICLR\u201916)","author":"Li Yujia","year":"2016","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2016. Gated graph sequence neural networks. In The International Conference on Learning Representations (ICLR\u201916). Caribe Hilton."},{"key":"e_1_3_2_305_2","volume-title":"The International Conference on Learning Representations Workshop","author":"Li Yujia","year":"2018","unstructured":"Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia. 2018. Learning deep generative models of graphs. In The International Conference on Learning Representations Workshop."},{"key":"e_1_3_2_306_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00236"},{"key":"e_1_3_2_307_2","first-page":"1","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","volume":"32","author":"Yun Seongjun","year":"2019","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph transformer networks. In The International Conference on Neural Information Processing Systems (NeurPS\u201919), Vol. 32. Curran Associates, Inc., 1\u201311."},{"key":"e_1_3_2_308_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290967"},{"key":"e_1_3_2_309_2","volume-title":"The International Conference on Learning Representations (ICLR\u201920)","author":"Zhang Yuyu","year":"2020","unstructured":"Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, and Le Song. 2020. Efficient probabilistic logic reasoning with graph neural networks. In The International Conference on Learning Representations (ICLR\u201920). Addis Ababa."},{"key":"e_1_3_2_310_2","unstructured":"Yuzhou Chen Yulia R. Gel and Konstanin Avrachenkov. 2020. Fractional graph convolutional networks (FGCN) for semi-supervised learning. (2020). https:\/\/openreview.net\/forum?id=BygacxrFwS."},{"key":"e_1_3_2_311_2","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","volume":"32","author":"Zhang Muhan","year":"2019","unstructured":"Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen. 2019. D-VAE: A variational autoencoder for directed acyclic graphs. In The International Conference on Neural Information Processing Systems (NeurPS\u201919), Vol. 32. Curran Associates, Inc."},{"key":"e_1_3_2_312_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_313_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/438"},{"key":"e_1_3_2_314_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.11.002"},{"key":"e_1_3_2_315_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.045"},{"key":"e_1_3_2_316_2","first-page":"241","volume-title":"The Annual Meeting of the Association for Computational Linguistics (ACL\u201919)","author":"Guo Zhijiang","year":"2019","unstructured":"Zhijiang Guo, Yan Zhang, and Wei Lu. 2019. Attention guided graph convolutional networks for relation extraction. In The Annual Meeting of the Association for Computational Linguistics (ACL\u201919). ACL, 241\u2013251."},{"key":"e_1_3_2_317_2","volume-title":"The International Conference on Machine Learning Workshop","author":"Deng Zhijie","year":"2019","unstructured":"Zhijie Deng, Yinpeng Dong, and Jun Zhu. 2019. Batch virtual adversarial training for graph convolutional networks. In The International Conference on Machine Learning Workshop. PMLR."},{"key":"e_1_3_2_318_2","first-page":"9244","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. GNNExplainer: Generating explanations for graph neural networks. In The International Conference on Neural Information Processing Systems (NeurPS\u201919). Curran Associates, Inc., 9244\u20139255."},{"key":"e_1_3_2_319_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_2_320_2","volume-title":"The International Conference on Neural Information Processing Systems (NIPS\u201918)","author":"Li Zhuwen","year":"2018","unstructured":"Zhuwen Li, Qifeng Chen, and Vladlen Koltun. 2018. Combinatorial optimization with graph convolutional networks and guided tree search. In The International Conference on Neural Information Processing Systems (NIPS\u201918). Curran Associates, Inc., 573\u2013546."},{"key":"e_1_3_2_321_2","article-title":"Pre-training graph neural networks for generic structural feature extraction","author":"Hu Ziniu","year":"2019","unstructured":"Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, and Yizhou Sun. 2019. Pre-training graph neural networks for generic structural feature extraction. arXiv (2019). https:\/\/arxiv.org\/abs\/1905.13728.","journal-title":"arXiv"},{"key":"e_1_3_2_322_2","volume-title":"The International Conference on Learning Representations Workshop","author":"Hu Ziniu","year":"2019","unstructured":"Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, and Yizhou Sun. 2019. Unsupervised pre-training of graph convolutional networks. In The International Conference on Learning Representations Workshop. https:\/\/arxiv.org\/abs\/1905.13728."},{"key":"e_1_3_2_323_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"e_1_3_2_324_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_325_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014424"},{"key":"e_1_3_2_326_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 34, 1 (2020), 249\u2013270. https:\/\/doi.org\/10.1109\/TKDE.2020.2981333","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_327_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_328_2","volume-title":"The International Conference on Neural Information Processing Systems (NeurPS\u201919)","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. In The International Conference on Neural Information Processing Systems (NeurPS\u201919), Vol. 32. Curran Associates, Inc."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3495161","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T09:09:02Z","timestamp":1645002542000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":327,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2,28]]}},"alternative-id":["10.1145\/3495161"],"URL":"http:\/\/dx.doi.org\/10.1145\/3495161","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":["Artificial Intelligence","Theoretical Computer Science"],"published":{"date-parts":[[2022,2,28]]}}}