{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T21:10:17Z","timestamp":1693429817433},"publisher-location":"New York, NY, USA","reference-count":119,"publisher":"ACM","funder":[{"name":"Tsinghua-Siemens Joint Research Center for Industrial Intelligence and Internet of Things"},{"name":"Natural Science Foundation of China","award":["61825602,62276148"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583472","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["CogDL: A Comprehensive Library for Graph Deep Learning"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5682-2810","authenticated-orcid":false,"given":"Yukuo","family":"Cen","sequence":"first","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1624-2149","authenticated-orcid":false,"given":"Zhenyu","family":"Hou","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6353-9706","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5923-8345","authenticated-orcid":false,"given":"Qibin","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7107-378X","authenticated-orcid":false,"given":"Yizhen","family":"Luo","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2064-8106","authenticated-orcid":false,"given":"Zhongming","family":"Yu","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5090-3177","authenticated-orcid":false,"given":"Hengrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5065-3016","authenticated-orcid":false,"given":"Xingcheng","family":"Yao","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8766-0153","authenticated-orcid":false,"given":"Aohan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2058-0626","authenticated-orcid":false,"given":"Shiguang","family":"Guo","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6092-2002","authenticated-orcid":false,"given":"Yuxiao","family":"Dong","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5058-4417","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5656-1083","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhipu AI, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0849-3252","authenticated-orcid":false,"given":"Guohao","family":"Dai","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6108-5157","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9241-702X","authenticated-orcid":false,"given":"Chang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0580-9728","authenticated-orcid":false,"given":"Hongxia","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3487-4593","authenticated-orcid":false,"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2022. PGL. https:\/\/github.com\/PaddlePaddle\/PGL 2022. PGL. https:\/\/github.com\/PaddlePaddle\/PGL"},{"key":"e_1_3_2_1_2_1","volume-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg\u00a0 S Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , 2016 . Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016). Mart\u00edn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg\u00a0S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)."},{"key":"e_1_3_2_1_3_1","volume-title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML\u201919. PMLR, 21\u201329.","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija , Bryan Perozzi , Amol Kapoor , Nazanin Alipourfard , Kristina Lerman , Hrayr Harutyunyan , Greg Ver\u00a0Steeg , and Aram Galstyan . 2019 . Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML\u201919. PMLR, 21\u201329. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver\u00a0Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML\u201919. PMLR, 21\u201329."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_1_5_1","volume-title":"Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261","author":"Battaglia W","year":"2018","unstructured":"Peter\u00a0 W Battaglia , Jessica\u00a0 B Hamrick , Victor Bapst , Alvaro Sanchez-Gonzalez , Vinicius Zambaldi , Mateusz Malinowski , Andrea Tacchetti , David Raposo , Adam Santoro , Ryan Faulkner , 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 ( 2018 ). Peter\u00a0W Battaglia, Jessica\u00a0B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_1_6_1","unstructured":"Lukas Biewald. 2020. Experiment Tracking with Weights and Biases. https:\/\/www.wandb.com\/ Software available from wandb.com. Lukas Biewald. 2020. Experiment Tracking with Weights and Biases. https:\/\/www.wandb.com\/ Software available from wandb.com."},{"key":"e_1_3_2_1_7_1","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Adversarial attacks on node embeddings via graph poisoning. In ICML\u201919. PMLR 695\u2013704. Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Adversarial attacks on node embeddings via graph poisoning. In ICML\u201919. PMLR 695\u2013704."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Aleksandar Bojchevski Johannes Klicpera Bryan Perozzi Amol Kapoor Martin Blais Benedek R\u00f3zemberczki Michal Lukasik and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate pagerank. In KDD\u201920. Aleksandar Bojchevski Johannes Klicpera Bryan Perozzi Amol Kapoor Martin Blais Benedek R\u00f3zemberczki Michal Lukasik and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate pagerank. In KDD\u201920.","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_1_9_1","unstructured":"Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NeurIPS\u201913. Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NeurIPS\u201913."},{"key":"e_1_3_2_1_10_1","volume-title":"The BioGRID interaction database. Nucleic acids research 36, suppl 1","author":"Breitkreutz Bobby-Joe","year":"2008","unstructured":"Bobby-Joe Breitkreutz , Chris Stark , 2008. The BioGRID interaction database. Nucleic acids research 36, suppl 1 ( 2008 ), D637\u2013D640. Bobby-Joe Breitkreutz, Chris Stark, 2008. The BioGRID interaction database. Nucleic acids research 36, suppl 1 (2008), D637\u2013D640."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_1_12_1","unstructured":"Shaosheng Cao Wei Lu and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In AAAI\u201916. Shaosheng Cao Wei Lu and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In AAAI\u201916."},{"key":"e_1_3_2_1_13_1","unstructured":"Yukuo Cen Xu Zou Jianwei Zhang Hongxia Yang Jingren Zhou and Jie Tang. 2019. Representation learning for attributed multiplex heterogeneous network. In KDD\u201919. 1358\u20131368. Yukuo Cen Xu Zou Jianwei Zhang Hongxia Yang Jingren Zhou and Jie Tang. 2019. Representation learning for attributed multiplex heterogeneous network. In KDD\u201919. 1358\u20131368."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_1_15_1","volume-title":"Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797","author":"Chen Jinyin","year":"2018","unstructured":"Jinyin Chen , Yangyang Wu , Xuanheng Xu , Yixian Chen , Haibin Zheng , and Qi Xuan . 2018. Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797 ( 2018 ). Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, and Qi Xuan. 2018. Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797 (2018)."},{"key":"e_1_3_2_1_16_1","unstructured":"Jianfei Chen Lianmin Zheng Zhewei Yao Dequan Wang Ion Stoica Michael\u00a0W Mahoney and Joseph\u00a0E Gonzalez. 2021. ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. In ICML\u201921. Jianfei Chen Lianmin Zheng Zhewei Yao Dequan Wang Ion Stoica Michael\u00a0W Mahoney and Joseph\u00a0E Gonzalez. 2021. ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. In ICML\u201921."},{"key":"e_1_3_2_1_17_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML\u201920. Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML\u201920."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_19_1","unstructured":"CSIRO\u2019s Data61. 2018. StellarGraph Machine Learning Library. https:\/\/github.com\/stellargraph\/stellargraph. CSIRO\u2019s Data61. 2018. StellarGraph Machine Learning Library. https:\/\/github.com\/stellargraph\/stellargraph."},{"key":"e_1_3_2_1_20_1","unstructured":"Michael Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS\u201916. Michael Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS\u201916."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh\u00a0V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD\u201917. Yuxiao Dong Nitesh\u00a0V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD\u201917.","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_22_1","volume-title":"Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982","author":"Dwivedi Vijay\u00a0Prakash","year":"2020","unstructured":"Vijay\u00a0Prakash Dwivedi , Chaitanya\u00a0 K Joshi , Thomas Laurent , Yoshua Bengio , and Xavier Bresson . 2020. Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 ( 2020 ). Vijay\u00a0Prakash Dwivedi, Chaitanya\u00a0K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2020. Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Negin Entezari Saba\u00a0A Al-Sayouri Amirali Darvishzadeh and Evangelos\u00a0E Papalexakis. 2020. All you need is low (rank) defending against adversarial attacks on graphs. In WSDM\u201920. 169\u2013177. Negin Entezari Saba\u00a0A Al-Sayouri Amirali Darvishzadeh and Evangelos\u00a0E Papalexakis. 2020. All you need is low (rank) defending against adversarial attacks on graphs. In WSDM\u201920. 169\u2013177.","DOI":"10.1145\/3336191.3371789"},{"key":"e_1_3_2_1_24_1","unstructured":"Federico Errica Marco Podda Davide Bacciu and Alessio Micheli. 2020. A fair comparison of graph neural networks for graph classification. In ICLR\u201920. Federico Errica Marco Podda Davide Bacciu and Alessio Micheli. 2020. A fair comparison of graph neural networks for graph classification. In ICLR\u201920."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW\u201919. Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW\u201919.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_26_1","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. In NeurIPS\u201920. 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. In NeurIPS\u201920."},{"key":"e_1_3_2_1_27_1","volume-title":"Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan\u00a0Eric Lenssen . 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 ( 2019 ). Matthias Fey and Jan\u00a0Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 (2019)."},{"key":"e_1_3_2_1_28_1","volume-title":"SIGN: Scalable Inception Graph Neural Networks. In ICML 2020 Workshop on GRL+.","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 GRL+. 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 GRL+."},{"key":"e_1_3_2_1_29_1","unstructured":"Tao-yang Fu Wang-Chien Lee and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In CIKM\u201917. Tao-yang Fu Wang-Chien Lee and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In CIKM\u201917."},{"key":"e_1_3_2_1_30_1","volume-title":"SC\u201920","author":"Gale Trevor","unstructured":"Trevor Gale , Matei Zaharia , Cliff Young , and Erich Elsen . 2020. Sparse gpu kernels for deep learning . In SC\u201920 . IEEE , 1\u201314. Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen. 2020. Sparse gpu kernels for deep learning. In SC\u201920. IEEE, 1\u201314."},{"key":"e_1_3_2_1_31_1","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In ICML\u201919. Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In ICML\u201919."},{"key":"e_1_3_2_1_32_1","volume-title":"Jraph: A library for graph neural networks in jax.http:\/\/github.com\/deepmind\/jraph","author":"Jonathan","year":"2020","unstructured":"Jonathan Godwin*, Thomas Keck*, Peter Battaglia , Victor Bapst , Thomas Kipf , Yujia Li , Kimberly Stachenfeld , Petar Veli\u010dkovi\u0107 , and Alvaro Sanchez-Gonzalez . 2020 . Jraph: A library for graph neural networks in jax.http:\/\/github.com\/deepmind\/jraph Jonathan Godwin*, Thomas Keck*, Peter Battaglia, Victor Bapst, Thomas Kipf, Yujia Li, Kimberly Stachenfeld, Petar Veli\u010dkovi\u0107, and Alvaro Sanchez-Gonzalez. 2020. Jraph: A library for graph neural networks in jax.http:\/\/github.com\/deepmind\/jraph"},{"key":"e_1_3_2_1_33_1","unstructured":"Ian\u00a0J Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In ICLR\u201915. Ian\u00a0J Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In ICLR\u201915."},{"key":"e_1_3_2_1_34_1","unstructured":"Marco Gori Gabriele Monfardini and Franco Scarselli. 2005. A new model for learning in graph domains. In IJCNN\u201905 Vol.\u00a02. 729\u2013734. Marco Gori Gabriele Monfardini and Franco Scarselli. 2005. A new model for learning in graph domains. In IJCNN\u201905 Vol.\u00a02. 729\u2013734."},{"key":"e_1_3_2_1_35_1","volume-title":"Graph neural networks in tensorflow and keras with spektral. arXiv preprint arXiv:2006.12138","author":"Grattarola Daniele","year":"2020","unstructured":"Daniele Grattarola and Cesare Alippi . 2020. Graph neural networks in tensorflow and keras with spektral. arXiv preprint arXiv:2006.12138 ( 2020 ). Daniele Grattarola and Cesare Alippi. 2020. Graph neural networks in tensorflow and keras with spektral. arXiv preprint arXiv:2006.12138 (2020)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD\u201916. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD\u201916.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_37_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS\u201917. 1025\u20131035. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS\u201917. 1025\u20131035."},{"key":"e_1_3_2_1_38_1","volume-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve.Radiology 143, 1","author":"Hanley A","year":"1982","unstructured":"James\u00a0 A Hanley and Barbara\u00a0 J McNeil . 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve.Radiology 143, 1 ( 1982 ). James\u00a0A Hanley and Barbara\u00a0J McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve.Radiology 143, 1 (1982)."},{"key":"e_1_3_2_1_39_1","unstructured":"Kaveh Hassani and Amir\u00a0Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In ICML\u201920. Kaveh Hassani and Amir\u00a0Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In ICML\u201920."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3115017"},{"key":"e_1_3_2_1_41_1","unstructured":"Zhenyu Hou Xiao Liu Yukuo Cen Yuxiao Dong Hongxia Yang Chunjie Wang and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In KDD\u201922. Zhenyu Hou Xiao Liu Yukuo Cen Yuxiao Dong Hongxia Yang Chunjie Wang and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In KDD\u201922."},{"key":"e_1_3_2_1_42_1","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 NeurIPS\u201920. 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 NeurIPS\u201920."},{"key":"e_1_3_2_1_43_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In KDD\u201920. Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In KDD\u201920."},{"key":"e_1_3_2_1_44_1","volume-title":"SC\u201920","author":"Huang Guyue","unstructured":"Guyue Huang , Guohao Dai , Yu Wang , and Huazhong Yang . 2020. GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks . In SC\u201920 . IEEE Press , Article 72. Guyue Huang, Guohao Dai, Yu Wang, and Huazhong Yang. 2020. GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks. In SC\u201920. IEEE Press, Article 72."},{"key":"e_1_3_2_1_45_1","unstructured":"Kezhao Huang Jidong Zhai Zhen Zheng Youngmin Yi and Xipeng Shen. 2021. Understanding and bridging the gaps in current GNN performance optimizations. In PPoPP\u201921. 119\u2013132. Kezhao Huang Jidong Zhai Zhen Zheng Youngmin Yi and Xipeng Shen. 2021. Understanding and bridging the gaps in current GNN performance optimizations. In PPoPP\u201921. 119\u2013132."},{"key":"e_1_3_2_1_46_1","volume-title":"Combining label propagation and simple models out-performs graph neural networks. arXiv preprint arXiv:2010.13993","author":"Huang Qian","year":"2020","unstructured":"Qian Huang , Horace He , Abhay Singh , Ser-Nam Lim , and Austin\u00a0 R Benson . 2020. Combining label propagation and simple models out-performs graph neural networks. arXiv preprint arXiv:2010.13993 ( 2020 ). Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin\u00a0R Benson. 2020. Combining label propagation and simple models out-performs graph neural networks. arXiv preprint arXiv:2010.13993 (2020)."},{"key":"e_1_3_2_1_47_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf N","year":"2016","unstructured":"Thomas\u00a0 N Kipf and Max Welling . 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas\u00a0N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_48_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR\u201917. Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR\u201917."},{"key":"e_1_3_2_1_49_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR\u201919. Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR\u201919."},{"key":"e_1_3_2_1_50_1","unstructured":"Johannes Klicpera Stefan Wei\u00dfenberger and Stephan G\u00fcnnemann. 2019. Diffusion Improves Graph Learning. In NeurIPS\u201919. Johannes Klicpera Stefan Wei\u00dfenberger and Stephan G\u00fcnnemann. 2019. Diffusion Improves Graph Learning. In NeurIPS\u201919."},{"key":"e_1_3_2_1_51_1","unstructured":"Junhyun Lee Inyeop Lee and Jaewoo Kang. 2019. Self-attention graph pooling. In ICML\u201919. Junhyun Lee Inyeop Lee and Jaewoo Kang. 2019. Self-attention graph pooling. In ICML\u201919."},{"key":"e_1_3_2_1_52_1","unstructured":"Guohao Li Matthias M\u00fcller Bernard Ghanem and Vladlen Koltun. 2021. Training graph neural networks with 1000 layers. In ICML\u201921. PMLR 6437\u20136449. Guohao Li Matthias M\u00fcller Bernard Ghanem and Vladlen Koltun. 2021. Training graph neural networks with 1000 layers. In ICML\u201921. PMLR 6437\u20136449."},{"key":"e_1_3_2_1_53_1","volume-title":"Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739","author":"Li Guohao","year":"2020","unstructured":"Guohao Li , Chenxin Xiong , Ali Thabet , and Bernard Ghanem . 2020 . Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739 (2020). Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem. 2020. Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739 (2020)."},{"key":"e_1_3_2_1_54_1","volume-title":"GACT: Activation Compressed Training for Generic Network Architectures. In ICML\u201922. PMLR.","author":"Liu Xiaoxuan","year":"2022","unstructured":"Xiaoxuan Liu , Lianmin Zheng , Dequan Wang , Yukuo Cen , Weize Chen , Xu Han , Jianfei Chen , Zhiyuan Liu , Jie Tang , Joey Gonzalez , 2022 . GACT: Activation Compressed Training for Generic Network Architectures. In ICML\u201922. PMLR. Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, 2022. GACT: Activation Compressed Training for Generic Network Architectures. In ICML\u201922. PMLR."},{"key":"e_1_3_2_1_55_1","unstructured":"Qingsong Lv Ming Ding Qiang Liu Yuxiang Chen Wenzheng Feng Siming He Chang Zhou Jianguo Jiang Yuxiao Dong and Jie Tang. 2021. Are we really making much progress? Revisiting benchmarking and refining heterogeneous graph neural networks. In KDD\u201921. 1150\u20131160. Qingsong Lv Ming Ding Qiang Liu Yuxiang Chen Wenzheng Feng Siming He Chang Zhou Jianguo Jiang Yuxiao Dong and Jie Tang. 2021. Are we really making much progress? Revisiting benchmarking and refining heterogeneous graph neural networks. In KDD\u201921. 1150\u20131160."},{"key":"e_1_3_2_1_56_1","unstructured":"Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019. Disentangled graph convolutional networks. In ICML\u201919. PMLR 4212\u20134221. Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019. Disentangled graph convolutional networks. In ICML\u201919. PMLR 4212\u20134221."},{"key":"e_1_3_2_1_57_1","unstructured":"Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In ICLR\u201918. Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In ICLR\u201918."},{"key":"e_1_3_2_1_58_1","volume-title":"graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005","author":"Narayanan Annamalai","year":"2017","unstructured":"Annamalai Narayanan , Mahinthan Chandramohan , Rajasekar Venkatesan , Lihui Chen , Yang Liu , and Shantanu Jaiswal . 2017. graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 ( 2017 ). Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)."},{"key":"e_1_3_2_1_59_1","volume-title":"GPU Technology Conference.","author":"Naumov Maxim","year":"2010","unstructured":"Maxim Naumov , L Chien , Philippe Vandermersch , and Ujval Kapasi . 2010 . Cusparse library . In GPU Technology Conference. Maxim Naumov, L Chien, Philippe Vandermersch, and Ujval Kapasi. 2010. Cusparse library. In GPU Technology Conference."},{"key":"e_1_3_2_1_60_1","unstructured":"Mathias Niepert Mohamed Ahmed and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In ICML\u201916. Mathias Niepert Mohamed Ahmed and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In ICML\u201916."},{"key":"e_1_3_2_1_61_1","unstructured":"Mingdong Ou Peng Cui Jian Pei Ziwei Zhang and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In KDD\u201916. 1105\u20131114. Mingdong Ou Peng Cui Jian Pei Ziwei Zhang and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In KDD\u201916. 1105\u20131114."},{"key":"e_1_3_2_1_62_1","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga 2019. PyTorch: An imperative style high-performance deep learning library. In NeurIPS\u201919. 8024\u20138035. Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga 2019. PyTorch: An imperative style high-performance deep learning library. In NeurIPS\u201919. 8024\u20138035."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"crossref","unstructured":"Jinghua Piao Guozhen Zhang Fengli Xu Zhilong Chen and Yong Li. 2021. Predicting customer value with social relationships via motif-based graph attention networks. In WWW\u201921. 3146\u20133157. Jinghua Piao Guozhen Zhang Fengli Xu Zhilong Chen and Yong Li. 2021. Predicting customer value with social relationships via motif-based graph attention networks. In WWW\u201921. 3146\u20133157.","DOI":"10.1145\/3442381.3449849"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_1_66_1","volume-title":"Netsmf: Large-scale network embedding as sparse matrix factorization. In WWW\u201919.","author":"Qiu Jiezhong","year":"2019","unstructured":"Jiezhong Qiu , Yuxiao Dong , Hao Ma , Jian Li , Chi Wang , Kuansan Wang , and Jie Tang . 2019 . Netsmf: Large-scale network embedding as sparse matrix factorization. In WWW\u201919. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. 2019. Netsmf: Large-scale network embedding as sparse matrix factorization. In WWW\u201919."},{"key":"e_1_3_2_1_67_1","unstructured":"Jiezhong Qiu Yuxiao Dong Hao Ma Jian Li Kuansan Wang and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk line pte and node2vec. In WSDM\u201918. Jiezhong Qiu Yuxiao Dong Hao Ma Jian Li Kuansan Wang and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk line pte and node2vec. In WSDM\u201918."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"e_1_3_2_1_69_1","volume-title":"Dropedge: Towards deep graph convolutional networks on node classification. In 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 ICLR\u201920. Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. Dropedge: Towards deep graph convolutional networks on node classification. In ICLR\u201920."},{"key":"e_1_3_2_1_70_1","volume-title":"ICDM\u201920","author":"Sankar Aravind","unstructured":"Aravind Sankar , Junting Wang , Adit Krishnan , and Hari Sundaram . 2020. Beyond localized graph neural networks: An attributed motif regularization framework . In ICDM\u201920 . IEEE , 472\u2013481. Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram. 2020. Beyond localized graph neural networks: An attributed motif regularization framework. In ICDM\u201920. IEEE, 472\u2013481."},{"key":"e_1_3_2_1_71_1","volume-title":"The graph neural network model","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah\u00a0Chung Tsoi , Markus Hagenbuchner , and Gabriele Monfardini . 2008. The graph neural network model . IEEE transactions on neural networks 20, 1 ( 2008 ), 61\u201380. Franco Scarselli, Marco Gori, Ah\u00a0Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61\u201380."},{"key":"e_1_3_2_1_72_1","volume-title":"Ivan Titov, and Max Welling.","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull , Thomas\u00a0 N Kipf , Peter Bloem , Rianne van\u00a0den Berg , Ivan Titov, and Max Welling. 2018 . Modeling relational data with graph convolutional networks. In European semantic web conference. Springer , 593\u2013607. Michael Schlichtkrull, Thomas\u00a0N Kipf, Peter Bloem, Rianne van\u00a0den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference. Springer, 593\u2013607."},{"key":"e_1_3_2_1_73_1","volume-title":"Collective classification in network data. AI magazine 29, 3","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Galligher , and Tina Eliassi-Rad . 2008. Collective classification in network data. AI magazine 29, 3 ( 2008 ), 93\u201393. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine 29, 3 (2008), 93\u201393."},{"key":"e_1_3_2_1_74_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur , Maximilian Mumme , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 ( 2018 ). Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_1_75_1","volume-title":"Scalable and adaptive graph neural networks with self-label-enhanced training. arXiv preprint arXiv:2104.09376","author":"Sun Chuxiong","year":"2021","unstructured":"Chuxiong Sun , Hongming Gu , and Jie Hu. 2021. Scalable and adaptive graph neural networks with self-label-enhanced training. arXiv preprint arXiv:2104.09376 ( 2021 ). Chuxiong Sun, Hongming Gu, and Jie Hu. 2021. Scalable and adaptive graph neural networks with self-label-enhanced training. arXiv preprint arXiv:2104.09376 (2021)."},{"key":"e_1_3_2_1_76_1","volume-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR\u201920.","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun , Jordan Hoffmann , Vikas Verma , and Jian Tang . 2020 . Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR\u201920. Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR\u201920."},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"crossref","unstructured":"Ke Sun Zhouchen Lin and Zhanxing Zhu. 2020. Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In AAAI\u201920 Vol.\u00a034. 5892\u20135899. Ke Sun Zhouchen Lin and Zhanxing Zhu. 2020. Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In AAAI\u201920 Vol.\u00a034. 5892\u20135899.","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"e_1_3_2_1_78_1","unstructured":"Zhiqing Sun Zhi-Hong Deng Jian-Yun Nie and Jian Tang. 2019. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In ICLR\u201919. Zhiqing Sun Zhi-Hong Deng Jian-Yun Nie and Jian Tang. 2019. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In ICLR\u201919."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783307"},{"key":"e_1_3_2_1_80_1","volume-title":"Line: Large-scale information network embedding. In WWW\u201915.","author":"Tang Jian","year":"2015","unstructured":"Jian Tang , Meng Qu , Mingzhe Wang , Ming Zhang , Jun Yan , and Qiaozhu Mei . 2015 . Line: Large-scale information network embedding. In WWW\u201915. Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In WWW\u201915."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"crossref","unstructured":"Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In KDD\u201908. Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In KDD\u201908.","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"crossref","unstructured":"Lei Tang and Huan Liu. 2009. Relational learning via latent social dimensions. In KDD\u201909. Lei Tang and Huan Liu. 2009. Relational learning via latent social dimensions. In KDD\u201909.","DOI":"10.1145\/1557019.1557109"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0210-x"},{"key":"e_1_3_2_1_84_1","unstructured":"Th\u00e9o Trouillon Johannes Welbl Sebastian Riedel \u00c9ric Gaussier and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In ICML\u201916. Th\u00e9o Trouillon Johannes Welbl Sebastian Riedel \u00c9ric Gaussier and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In ICML\u201916."},{"key":"e_1_3_2_1_85_1","unstructured":"Shikhar Vashishth Soumya Sanyal Vikram Nitin and Partha Talukdar. 2020. Composition-based multi-relational graph convolutional networks. In ICLR\u201920. Shikhar Vashishth Soumya Sanyal Vikram Nitin and Partha Talukdar. 2020. Composition-based multi-relational graph convolutional networks. In ICLR\u201920."},{"key":"e_1_3_2_1_86_1","unstructured":"Petar Velickovic William Fedus William\u00a0L Hamilton Pietro Li\u00f2 Yoshua Bengio and R\u00a0Devon Hjelm. 2019. Deep Graph Infomax. In ICLR\u201919. Petar Velickovic William Fedus William\u00a0L Hamilton Pietro Li\u00f2 Yoshua Bengio and R\u00a0Devon Hjelm. 2019. Deep Graph Infomax. In ICLR\u201919."},{"key":"e_1_3_2_1_87_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR\u201918. Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR\u201918."},{"key":"e_1_3_2_1_88_1","volume-title":"Graphmix: Improved training of gnns for semi-supervised learning. In AAAI\u201921, Vol.\u00a035. 10024\u201310032.","author":"Verma Vikas","year":"2021","unstructured":"Vikas Verma , Meng Qu , Kenji Kawaguchi , Alex Lamb , Yoshua Bengio , Juho Kannala , and Jian Tang . 2021 . Graphmix: Improved training of gnns for semi-supervised learning. In AAAI\u201921, Vol.\u00a035. 10024\u201310032. Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang. 2021. Graphmix: Improved training of gnns for semi-supervised learning. In AAAI\u201921, Vol.\u00a035. 10024\u201310032."},{"key":"e_1_3_2_1_89_1","volume-title":"Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906.06532","author":"Wang Chun","year":"2019","unstructured":"Chun Wang , Shirui Pan , Ruiqi Hu , Guodong Long , Jing Jiang , and Chengqi Zhang . 2019. Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906.06532 ( 2019 ). Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906.06532 (2019)."},{"key":"e_1_3_2_1_90_1","unstructured":"Daixin Wang Peng Cui and Wenwu Zhu. 2016. Structural deep network embedding. In KDD\u201916. 1225\u20131234. Daixin Wang Peng Cui and Wenwu Zhu. 2016. Structural deep network embedding. In KDD\u201916. 1225\u20131234."},{"key":"e_1_3_2_1_91_1","volume-title":"Deep graph library: Towards efficient and scalable deep learning on graphs. arXiv preprint arXiv:1909.01315","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 , 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. arXiv preprint arXiv:1909.01315 ( 2019 ). Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. arXiv preprint arXiv:1909.01315 (2019)."},{"key":"e_1_3_2_1_92_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip\u00a0S Yu. 2019. Heterogeneous graph attention network. In WWW\u201919. 2022\u20132032. Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip\u00a0S Yu. 2019. Heterogeneous graph attention network. In WWW\u201919. 2022\u20132032.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_93_1","unstructured":"Yuke Wang Boyuan Feng Gushu Li Shuangchen Li Lei Deng Yuan Xie and Yufei Ding. 2021. GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs. In OSDI\u201921. Yuke Wang Boyuan Feng Gushu Li Shuangchen Li Lei Deng Yuan Xie and Yufei Ding. 2021. GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs. In OSDI\u201921."},{"key":"e_1_3_2_1_94_1","volume-title":"Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog) 38, 5","author":"Wang Yue","year":"2019","unstructured":"Yue Wang , Yongbin Sun , Ziwei Liu , Sanjay\u00a0 E Sarma , Michael\u00a0 M Bronstein , and Justin\u00a0 M Solomon . 2019. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog) 38, 5 ( 2019 ), 1\u201312. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay\u00a0E Sarma, Michael\u00a0M Bronstein, and Justin\u00a0M Solomon. 2019. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog) 38, 5 (2019), 1\u201312."},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-017-0290-3"},{"key":"e_1_3_2_1_96_1","unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML\u201919. Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML\u201919."},{"key":"e_1_3_2_1_97_1","unstructured":"Jiarong Xu Yizhou Sun Xin Jiang Yanhao Wang Chunping Wang Jiangang Lu and Yang Yang. 2022. Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs. In AAAI\u201922. Jiarong Xu Yizhou Sun Xin Jiang Yanhao Wang Chunping Wang Jiangang Lu and Yang Yang. 2022. Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs. In AAAI\u201922."},{"key":"e_1_3_2_1_98_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR\u201919. Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR\u201919."},{"key":"e_1_3_2_1_99_1","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 ICML\u201918. Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In ICML\u201918."},{"key":"e_1_3_2_1_100_1","doi-asserted-by":"crossref","unstructured":"Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In KDD\u201915. Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In KDD\u201915.","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_1_101_1","volume-title":"X. He, Jianfeng Gao, and L. Deng.","author":"Yang B.","year":"2015","unstructured":"B. Yang , Wen tau Yih , X. He, Jianfeng Gao, and L. Deng. 2015 . Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In ICLR\u2019 15. B. Yang, Wen tau Yih, X. He, Jianfeng Gao, and L. Deng. 2015. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In ICLR\u201915."},{"key":"e_1_3_2_1_102_1","unstructured":"Zhilin Yang William Cohen and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML\u201916. PMLR 40\u201348. Zhilin Yang William Cohen and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML\u201916. PMLR 40\u201348."},{"key":"e_1_3_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833670"},{"key":"e_1_3_2_1_104_1","doi-asserted-by":"crossref","unstructured":"Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William\u00a0L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD\u201918. 974\u2013983. Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William\u00a0L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD\u201918. 974\u2013983.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_105_1","unstructured":"Rex Ying Jiaxuan You Christopher Morris Xiang Ren William\u00a0L Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In NeurIPS\u201918. Rex Ying Jiaxuan You Christopher Morris Xiang Ren William\u00a0L Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In NeurIPS\u201918."},{"key":"e_1_3_2_1_106_1","unstructured":"Jiaxuan You Zhitao Ying and Jure Leskovec. 2020. Design space for graph neural networks. In NeurIPS\u201920. Jiaxuan You Zhitao Ying and Jure Leskovec. 2020. Design space for graph neural networks. In NeurIPS\u201920."},{"key":"e_1_3_2_1_107_1","unstructured":"Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo\u00a0J Kim. 2019. Graph transformer networks. In NeurIPS\u201919. Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo\u00a0J Kim. 2019. Graph transformer networks. In NeurIPS\u201919."},{"key":"e_1_3_2_1_108_1","unstructured":"Reza Zafarani and Huan Liu. 2009. Social computing data repository at ASU. Reza Zafarani and Huan Liu. 2009. Social computing data repository at ASU."},{"key":"e_1_3_2_1_109_1","unstructured":"Hanqing Zeng Hongkuan Zhou Ajitesh Srivastava Rajgopal Kannan and Viktor Prasanna. 2020. GraphSAINT: Graph Sampling Based Inductive Learning Method. In ICLR\u201920. Hanqing Zeng Hongkuan Zhou Ajitesh Srivastava Rajgopal Kannan and Viktor Prasanna. 2020. GraphSAINT: Graph Sampling Based Inductive Learning Method. In ICLR\u201920."},{"key":"e_1_3_2_1_110_1","doi-asserted-by":"crossref","unstructured":"Jie Zhang Yuxiao Dong Yan Wang Jie Tang and Ming Ding. 2019. ProNE: fast and scalable network representation learning. In IJCAI\u201919. 4278\u20134284. Jie Zhang Yuxiao Dong Yan Wang Jie Tang and Ming Ding. 2019. ProNE: fast and scalable network representation learning. In IJCAI\u201919. 4278\u20134284.","DOI":"10.24963\/ijcai.2019\/594"},{"key":"e_1_3_2_1_111_1","doi-asserted-by":"crossref","unstructured":"Muhan Zhang Zhicheng Cui Marion Neumann and Yixin Chen. 2018. An end-to-end deep learning architecture for graph classification. In AAAI\u201918. Muhan Zhang Zhicheng Cui Marion Neumann and Yixin Chen. 2018. An end-to-end deep learning architecture for graph classification. In AAAI\u201918.","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"e_1_3_2_1_112_1","doi-asserted-by":"crossref","unstructured":"Xiaotong Zhang Han Liu Qimai Li and Xiao-Ming Wu. 2019. Attributed graph clustering via adaptive graph convolution. In IJCAI\u201919. Xiaotong Zhang Han Liu Qimai Li and Xiao-Ming Wu. 2019. Attributed graph clustering via adaptive graph convolution. In IJCAI\u201919.","DOI":"10.24963\/ijcai.2019\/601"},{"key":"e_1_3_2_1_113_1","volume-title":"Gnnguard: Defending graph neural networks against adversarial attacks. In NeurIPS\u201920, Vol.\u00a033. 9263\u20139275.","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik . 2020 . Gnnguard: Defending graph neural networks against adversarial attacks. In NeurIPS\u201920, Vol.\u00a033. 9263\u20139275. Xiang Zhang and Marinka Zitnik. 2020. Gnnguard: Defending graph neural networks against adversarial attacks. In NeurIPS\u201920, Vol.\u00a033. 9263\u20139275."},{"key":"e_1_3_2_1_114_1","volume-title":"KDD CUP 2020 ML Track 2 Adversarial Attacks and Defense on Academic Graph 1st Place Solution.","author":"Zheng Qinkai","year":"2020","unstructured":"Qinkai Zheng , Yixiao Fei , Yanhao Li , Qingmin Liu , Minhao Hu , and Qibo Sun . 2020 . KDD CUP 2020 ML Track 2 Adversarial Attacks and Defense on Academic Graph 1st Place Solution. Qinkai Zheng, Yixiao Fei, Yanhao Li, Qingmin Liu, Minhao Hu, and Qibo Sun. 2020. KDD CUP 2020 ML Track 2 Adversarial Attacks and Defense on Academic Graph 1st Place Solution."},{"key":"e_1_3_2_1_115_1","unstructured":"Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust graph convolutional networks against adversarial attacks. In KDD\u201919. 1399\u20131407. Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust graph convolutional networks against adversarial attacks. In KDD\u201919. 1399\u20131407."},{"key":"e_1_3_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352127"},{"key":"e_1_3_2_1_117_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu , Yichen Xu , Feng Yu , Qiang Liu , Shu Wu , and Liang Wang . 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 ( 2020 ). Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)."},{"key":"e_1_3_2_1_118_1","volume-title":"TDGIA: Effective injection attacks on graph neural networks. In KDD\u201921. 2461\u20132471.","author":"Zou Xu","year":"2021","unstructured":"Xu Zou , Qinkai Zheng , Yuxiao Dong , Xinyu Guan , Evgeny Kharlamov , Jialiang Lu , and Jie Tang . 2021 . TDGIA: Effective injection attacks on graph neural networks. In KDD\u201921. 2461\u20132471. Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang. 2021. TDGIA: Effective injection attacks on graph neural networks. In KDD\u201921. 2461\u20132471."},{"key":"e_1_3_2_1_119_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial attacks on neural networks for graph data. In KDD\u201918. 2847\u20132856. Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial attacks on neural networks for graph data. In KDD\u201918. 2847\u20132856.","DOI":"10.1145\/3219819.3220078"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T20:48:08Z","timestamp":1693428488000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":119,"alternative-id":["10.1145\/3543507.3583472","10.1145\/3543507"],"URL":"http:\/\/dx.doi.org\/10.1145\/3543507.3583472","relation":{},"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}