{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:09:45Z","timestamp":1765544985635,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679993","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"4020-4025","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Scalable Expressiveness through Preprocessed Graph Perturbations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2261-569X","authenticated-orcid":false,"given":"Danial","family":"Saber","sequence":"first","affiliation":[{"name":"Ontario Tech University, Oshawa, Ontario, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8053-7710","authenticated-orcid":false,"given":"Amirali","family":"Salehi-Abari","sequence":"additional","affiliation":[{"name":"Ontario Tech University, Oshawa, Ontario, Canada"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"A survey on graph neural networks for knowledge graph completion. arXiv preprint arXiv:2007.12374","author":"Arora Siddhant","year":"2020","unstructured":"Siddhant Arora. 2020. A survey on graph neural networks for knowledge graph completion. arXiv preprint arXiv:2007.12374 (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"Diffusion-convolutional neural networks. Advances in neural information processing systems","author":"Atwood James","year":"2016","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_1_3_1","volume-title":"Expressive power of invariant and equivariant graph neural networks. arXiv preprint arXiv:2006.15646","author":"Azizian Waiss","year":"2020","unstructured":"Waiss Azizian and Marc Lelarge. 2020. Expressive power of invariant and equivariant graph neural networks. arXiv preprint arXiv:2006.15646 (2020)."},{"key":"e_1_3_2_1_4_1","first-page":"25280","article-title":"Graph neural networks with local graph parameters","volume":"34","author":"Barcel\u00f3 Pablo","year":"2021","unstructured":"Pablo Barcel\u00f3, Floris Geerts, Juan Reutter, and Maksimilian Ryschkov. 2021. Graph neural networks with local graph parameters. Advances in Neural Information Processing Systems, Vol. 34 (2021), 25280--25293.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_5_1","volume-title":"Equivariant subgraph aggregation networks. arXiv preprint arXiv:2110.02910","author":"Bevilacqua Beatrice","year":"2021","unstructured":"Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron. 2021. Equivariant subgraph aggregation networks. arXiv preprint arXiv:2110.02910 (2021)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3154319"},{"key":"e_1_3_2_1_8_1","first-page":"19746","article-title":"Graph neural networks with adaptive readouts","volume":"35","author":"Buterez David","year":"2022","unstructured":"David Buterez, Jon Paul Janet, Steven J Kiddle, Dino Oglic, and Pietro Li\u00f2. 2022. Graph neural networks with adaptive readouts. Advances in Neural Information Processing Systems, Vol. 35 (2022), 19746--19758.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287","author":"Cangea Cuatualina","year":"2018","unstructured":"Cuatualina Cangea, Petar Velivckovi\u0107, Nikola Jovanovi\u0107, Thomas Kipf, and Pietro Li\u00f2. 2018. Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287 (2018)."},{"key":"e_1_3_2_1_10_1","volume-title":"Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)."},{"key":"e_1_3_2_1_11_1","unstructured":"Jianfei Chen and Jun Zhu. 2018. Stochastic training of graph convolutional networks. (2018)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_13_1","first-page":"1713","article-title":"Reconstruction for powerful graph representations","volume":"34","author":"Cotta Leonardo","year":"2021","unstructured":"Leonardo Cotta, Christopher Morris, and Bruno Ribeiro. 2021. Reconstruction for powerful graph representations. Advances in Neural Information Processing Systems, Vol. 34 (2021), 1713--1726.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(03)00628-4"},{"key":"e_1_3_2_1_16_1","volume-title":"Graph Neural Networks with Learnable Structural and Positional Representations. In International Conference on Learning Representations.","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2022. Graph Neural Networks with Learnable Structural and Positional Representations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","volume-title":"Graph random neural networks for semi-supervised learning on graphs. Advances in neural information processing systems","author":"Feng Wenzheng","year":"2020","unstructured":"Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph random neural networks for semi-supervised learning on graphs. Advances in neural information processing systems, Vol. 33 (2020), 22092--22103."},{"key":"e_1_3_2_1_18_1","first-page":"31376","article-title":"Understanding and extending subgraph gnns by rethinking their symmetries","volume":"35","author":"Frasca Fabrizio","year":"2022","unstructured":"Fabrizio Frasca, Beatrice Bevilacqua, Michael Bronstein, and Haggai Maron. 2022. Understanding and extending subgraph gnns by rethinking their symmetries. Advances in Neural Information Processing Systems, Vol. 35 (2022), 31376--31390.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_19_1","volume-title":"SIGN: Scalable Inception Graph Neural Networks. In ICML 2020 Workshop on Graph Representation Learning and Beyond.","author":"Frasca Fabrizio","year":"2020","unstructured":"Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Benjamin Chamberlain, Michael Bronstein, and Federico Monti. 2020. SIGN: Scalable Inception Graph Neural Networks. In ICML 2020 Workshop on Graph Representation Learning and Beyond."},{"key":"e_1_3_2_1_20_1","volume-title":"international conference on machine learning. PMLR","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In international conference on machine learning. PMLR, 2083--2092."},{"key":"e_1_3_2_1_21_1","volume-title":"Jeremias Sulam, and Jeffrey J Gray.","author":"Gao Wenhao","year":"2020","unstructured":"Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J Gray. 2020. Deep learning in protein structural modeling and design. Patterns, Vol. 1, 9 (2020)."},{"key":"e_1_3_2_1_22_1","volume-title":"International conference on machine learning. PMLR, 1263--1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263--1272."},{"key":"e_1_3_2_1_23_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"volume-title":"Graph representation learning","author":"Hamilton William L","key":"e_1_3_2_1_24_1","unstructured":"William L Hamilton. 2020. Graph representation learning. Morgan & Claypool Publishers."},{"key":"e_1_3_2_1_25_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems","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. Advances in neural information processing systems, Vol. 33 (2020), 22118--22133."},{"key":"e_1_3_2_1_26_1","volume-title":"Adaptive sampling towards fast graph representation learning. Advances in neural information processing systems","author":"Huang Wenbing","year":"2018","unstructured":"Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018. Adaptive sampling towards fast graph representation learning. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_27_1","volume-title":"Boosting the Cycle Counting Power of Graph Neural Networks with $textI^2$-GNNs. arXiv preprint arXiv:2210.13978","author":"Huang Yinan","year":"2022","unstructured":"Yinan Huang, Xingang Peng, Jianzhu Ma, and Muhan Zhang. 2022. Boosting the Cycle Counting Power of Graph Neural Networks with $textI^2$-GNNs. arXiv preprint arXiv:2210.13978 (2022)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615227"},{"key":"e_1_3_2_1_29_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_30_1","volume-title":"Understanding attention and generalization in graph neural networks. Advances in neural information processing systems","author":"Knyazev Boris","year":"2019","unstructured":"Boris Knyazev, Graham W Taylor, and Mohamed Amer. 2019. Understanding attention and generalization in graph neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_31_1","volume-title":"International conference on machine learning. PMLR, 3734--3743","author":"Lee Junhyun","year":"2019","unstructured":"Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019. Self-attention graph pooling. In International conference on machine learning. PMLR, 3734--3743."},{"key":"e_1_3_2_1_32_1","volume-title":"International conference on machine learning. PMLR, 3744--3753","author":"Lee Juho","year":"2019","unstructured":"Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. 2019. Set transformer: A framework for attention-based permutation-invariant neural networks. In International conference on machine learning. PMLR, 3744--3753."},{"key":"e_1_3_2_1_33_1","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"Leman AA","year":"1968","unstructured":"AA Leman and Boris Weisfeiler. 1968. A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsiya, Vol. 2, 9 (1968), 12--16.","journal-title":"Nauchno-Technicheskaya Informatsiya"},{"key":"e_1_3_2_1_34_1","first-page":"4465","article-title":"Distance encoding: Design provably more powerful neural networks for graph representation learning","volume":"33","author":"Li Pan","year":"2020","unstructured":"Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. 2020. Distance encoding: Design provably more powerful neural networks for graph representation learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 4465--4478.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_35_1","volume-title":"International conference on machine learning. PMLR, 3835--3845","author":"Li Yujia","year":"2019","unstructured":"Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli. 2019. Graph matching networks for learning the similarity of graph structured objects. In International conference on machine learning. PMLR, 3835--3845."},{"key":"e_1_3_2_1_36_1","volume-title":"Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493","author":"Li Yujia","year":"2015","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557688"},{"key":"e_1_3_2_1_38_1","volume-title":"Shweta Ann Jacob, and Amirali Salehi-Abari","author":"Louis Paul","year":"2023","unstructured":"Paul Louis, Shweta Ann Jacob, and Amirali Salehi-Abari. 2023. Simplifying subgraph representation learning for scalable link prediction. arXiv preprint arXiv:2301.12562 (2023)."},{"key":"e_1_3_2_1_39_1","volume-title":"Provably powerful graph networks. Advances in neural information processing systems","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. Provably powerful graph networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_40_1","volume-title":"Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902","author":"Maron Haggai","year":"2018","unstructured":"Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. 2018. Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902 (2018)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.42"},{"key":"e_1_3_2_1_42_1","volume-title":"ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. 2020. TUDataset: A collection of benchmark datasets for learning with graphs. In ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL 2020)."},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning. PMLR, 16017--16042","author":"Morris Christopher","year":"2022","unstructured":"Christopher Morris, Gaurav Rattan, Sandra Kiefer, and Siamak Ravanbakhsh. 2022. Speqnets: Sparsity-aware permutation-equivariant graph networks. In International Conference on Machine Learning. PMLR, 16017--16042."},{"key":"e_1_3_2_1_44_1","first-page":"21824","article-title":"Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings","volume":"33","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris, Gaurav Rattan, and Petra Mutzel. 2020. Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings. Advances in Neural Information Processing Systems, Vol. 33 (2020), 21824--21840.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_1_46_1","volume-title":"Improving Peer Assessment with Graph Neural Networks","author":"Namanloo Alireza A","year":"2022","unstructured":"Alireza A Namanloo, Julie Thorpe, and Amirali Salehi-Abari. 2022. Improving Peer Assessment with Graph Neural Networks. International Educational Data Mining Society (2022)."},{"key":"e_1_3_2_1_47_1","volume-title":"International conference on machine learning. PMLR","author":"Niepert Mathias","year":"2016","unstructured":"Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In International conference on machine learning. PMLR, 2014--2023."},{"key":"e_1_3_2_1_48_1","first-page":"21997","article-title":"DropGNN: Random dropouts increase the expressiveness of graph neural networks","volume":"34","author":"Papp P\u00e1l Andr\u00e1s","year":"2021","unstructured":"P\u00e1l Andr\u00e1s Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer. 2021. DropGNN: Random dropouts increase the expressiveness of graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 21997--22009.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Machine Learning. PMLR, 17323--17345","author":"Papp P\u00e1l Andr\u00e1s","year":"2022","unstructured":"P\u00e1l Andr\u00e1s Papp and Roger Wattenhofer. 2022. A theoretical comparison of graph neural network extensions. In International Conference on Machine Learning. PMLR, 17323--17345."},{"key":"e_1_3_2_1_50_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations.","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 International Conference on Learning Representations."},{"key":"e_1_3_2_1_51_1","article-title":"Weisfeiler-lehman graph kernels","volume":"12","author":"Shervashidze Nino","year":"2011","unstructured":"Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt. 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, Vol. 12, 9 (2011).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_52_1","volume-title":"LMC: Fast training of GNNs via subgraph sampling with provable convergence. arXiv preprint arXiv:2302.00924","author":"Shi Zhihao","year":"2023","unstructured":"Zhihao Shi, Xize Liang, and Jie Wang. 2023. LMC: Fast training of GNNs via subgraph sampling with provable convergence. arXiv preprint arXiv:2302.00924 (2023)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg130"},{"key":"e_1_3_2_1_54_1","volume-title":"Ira Ktena, Petar Velivckovi\u0107, and Sreenivas Gollapudi.","author":"Velingker Ameya","year":"2022","unstructured":"Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Velivckovi\u0107, and Sreenivas Gollapudi. 2022. Affinity-aware graph networks. arXiv preprint arXiv:2206.11941 (2022)."},{"key":"e_1_3_2_1_55_1","volume-title":"International conference on machine learning. PMLR, 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861--6871."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494523"},{"key":"e_1_3_2_1_57_1","volume-title":"International Conference on Learning Representations.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_58_1","volume-title":"International conference on machine learning. PMLR, 5453--5462","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In International conference on machine learning. PMLR, 5453--5462."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_61_1","volume-title":"Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17283"},{"key":"e_1_3_2_1_63_1","volume-title":"Deep sets. Advances in neural information processing systems","author":"Zaheer Manzil","year":"2017","unstructured":"Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola. 2017. Deep sets. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_64_1","volume-title":"Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931 (2019)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"e_1_3_2_1_66_1","first-page":"15734","article-title":"Nested graph neural networks","volume":"34","author":"Zhang Muhan","year":"2021","unstructured":"Muhan Zhang and Pan Li. 2021. Nested graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 15734--15747.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_67_1","first-page":"9061","article-title":"Labeling trick: A theory of using graph neural networks for multi-node representation learning","volume":"34","author":"Zhang Muhan","year":"2021","unstructured":"Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021. Labeling trick: A theory of using graph neural networks for multi-node representation learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 9061--9073.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_68_1","volume-title":"International Conference on Machine Learning. PMLR, 26467--26483","author":"Zhang Wentao","year":"2022","unstructured":"Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, and Bin Cui. 2022. NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning. In International Conference on Machine Learning. PMLR, 26467--26483."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512008"},{"key":"e_1_3_2_1_70_1","volume-title":"International Conference on Learning Representations.","author":"Zhao Lingxiao","year":"2022","unstructured":"Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah. 2022. From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_71_1","volume-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks. Advances in neural information processing systems","author":"Zou Difan","year":"2019","unstructured":"Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu. 2019. Layer-dependent importance sampling for training deep and large graph convolutional networks. Advances in neural information processing systems, Vol. 32 (2019)."}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679993","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679993","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:16Z","timestamp":1750294696000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":71,"alternative-id":["10.1145\/3627673.3679993","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679993","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}