{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:06:21Z","timestamp":1775912781812,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Natural Science Foundation of Jiangsu Province award","award":["BK20210181"],"award-info":[{"award-number":["BK20210181"]}]},{"name":"Key Research and Development Program of Jiangsu Province award","award":["BE2021729"],"award-info":[{"award-number":["BE2021729"]}]},{"name":"Open Research Projects of Zhejiang Lab award","award":["2022PG0AB07"],"award-info":[{"award-number":["2022PG0AB07"]}]},{"name":"National Natural Science Foundation of China award","award":["62102177;U1811461"],"award-info":[{"award-number":["62102177;U1811461"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557478","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"201-211","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Towards Self-supervised Learning on Graphs with Heterophily"],"prefix":"10.1145","author":[{"given":"Jingfan","family":"Chen","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Guanghui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Yifan","family":"Qi","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Chunfeng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Yihua","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"international conference on machine learning. PMLR, 21--29","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 international conference on machine learning. PMLR, 21--29 . 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 international conference on machine learning. PMLR, 21--29."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"540","author":"Belghazi Mohamed Ishmael","year":"2018","unstructured":"Mohamed Ishmael Belghazi , Aristide Baratin , Sai Rajeshwar , Sherjil Ozair , Yoshua Bengio , Aaron Courville , and Devon Hjelm . 2018 . Mutual Information Neural Estimation . In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 80). PMLR, 531-- 540 . Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual Information Neural Estimation. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80). PMLR, 531--540."},{"key":"e_1_3_2_1_3_1","volume-title":"Chawla","author":"Bielak Piotr","year":"2021","unstructured":"Piotr Bielak , Tomasz Kajdanowicz , and Nitesh V . Chawla . 2021 . Graph Barlow Twins : A self-supervised representation learning framework for graphs. arxiv: 2106.02466 [cs.LG] Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V. Chawla. 2021. Graph Barlow Twins: A self-supervised representation learning framework for graphs. arxiv: 2106.02466 [cs.LG]"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380214"},{"key":"e_1_3_2_1_5_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_1_6_1","volume-title":"Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988","author":"Chien Eli","year":"2020","unstructured":"Eli Chien , Jianhao Peng , Pan Li , and Olgica Milenkovic . 2020. Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988 ( 2020 ). Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2020. Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988 (2020)."},{"key":"e_1_3_2_1_7_1","volume-title":"Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization. arXiv preprint arXiv:2203.12265","author":"Dong Wei","year":"2022","unstructured":"Wei Dong , Junsheng Wu , Yi Luo , Zongyuan Ge , and Peng Wang . 2022. Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization. arXiv preprint arXiv:2203.12265 ( 2022 ). Wei Dong, Junsheng Wu, Yi Luo, Zongyuan Ge, and Peng Wang. 2022. Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization. arXiv preprint arXiv:2203.12265 (2022)."},{"key":"e_1_3_2_1_8_1","volume-title":"Layla El Asri, and Seyed Mehran Kazemi","author":"Fatemi Bahare","year":"2021","unstructured":"Bahare Fatemi , Layla El Asri, and Seyed Mehran Kazemi . 2021 . SLAPS : Self-Supervision Improves Structure Learning for Graph Neural Networks. Advances in Neural Information Processing Systems , Vol. 34 (2021). Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. 2021. SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PMLR","author":"Franceschi Luca","year":"2019","unstructured":"Luca Franceschi , Mathias Niepert , Massimiliano Pontil , and Xiao He . 2019 . Learning discrete structures for graph neural networks . In International conference on machine learning. PMLR , 1972--1982. Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019. Learning discrete structures for graph neural networks. In International conference on machine learning. PMLR, 1972--1982."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2019 . Predict then Propagate: Graph Neural Networks meet Personalized PageRank . In International Conference on Learning Representations (ICLR). Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_12_1","unstructured":"William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS.  William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS."},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Machine Learning. PMLR, 4116--4126","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi . 2020 . Contrastive multi-view representation learning on graphs . In International Conference on Machine Learning. PMLR, 4116--4126 . Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning. PMLR, 4116--4126."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2767599"},{"key":"e_1_3_2_1_15_1","volume-title":"Reducing the dimensionality of data with neural networks. science","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton and Ruslan R Salakhutdinov . 2006. Reducing the dimensionality of data with neural networks. science , Vol. 313 , 5786 ( 2006 ), 504--507. Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. science, Vol. 313, 5786 (2006), 504--507."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Learning Representations.","author":"Hou Yifan","year":"2019","unstructured":"Yifan Hou , Jian Zhang , James Cheng , Kaili Ma , Richard TB Ma , Hongzhi Chen , and Ming-Chang Yang . 2019 . Measuring and improving the use of graph information in graph neural networks . In International Conference on Learning Representations. Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard TB Ma, Hongzhi Chen, and Ming-Chang Yang. 2019. Measuring and improving the use of graph information in graph neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","volume-title":"Graph-MLP: node classification without message passing in graph. arXiv preprint arXiv:2106.04051","author":"Hu Yang","year":"2021","unstructured":"Yang Hu , Haoxuan You , Zhecan Wang , Zhicheng Wang , Erjin Zhou , and Yue Gao . 2021. Graph-MLP: node classification without message passing in graph. arXiv preprint arXiv:2106.04051 ( 2021 ). Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao. 2021. Graph-MLP: node classification without message passing in graph. arXiv preprint arXiv:2106.04051 (2021)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00031"},{"key":"e_1_3_2_1_19_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Jin Di","year":"2021","unstructured":"Di Jin , Zhizhi Yu , Cuiying Huo , Rui Wang , Xiao Wang , Dongxiao He , and Jiawei Han . 2021 b. Universal Graph Convolutional Networks . Advances in Neural Information Processing Systems , Vol. 34 (2021). Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, and Jiawei Han. 2021b. Universal Graph Convolutional Networks. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441735"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449971"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.06.009"},{"key":"e_1_3_2_1_23_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR). Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_25_1","volume-title":"A mutual information maximization perspective of language representation learning. arXiv preprint arXiv:1910.08350","author":"Kong Lingpeng","year":"2019","unstructured":"Lingpeng Kong , Cyprien de Masson d' Autume , Wang Ling , Lei Yu , Zihang Dai , and Dani Yogatama . 2019. A mutual information maximization perspective of language representation learning. arXiv preprint arXiv:1910.08350 ( 2019 ). Lingpeng Kong, Cyprien de Masson d'Autume, Wang Ling, Lei Yu, Zihang Dai, and Dani Yogatama. 2019. A mutual information maximization perspective of language representation learning. arXiv preprint arXiv:1910.08350 (2019)."},{"key":"e_1_3_2_1_26_1","volume-title":"New Benchmarks for Learning on Non-Homophilous Graphs. arXiv preprint arXiv:2104.01404","author":"Lim Derek","year":"2021","unstructured":"Derek Lim , Xiuyu Li , Felix Hohne , and Ser-Nam Lim . 2021. New Benchmarks for Learning on Non-Homophilous Graphs. arXiv preprint arXiv:2104.01404 ( 2021 ). Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim. 2021. New Benchmarks for Learning on Non-Homophilous Graphs. arXiv preprint arXiv:2104.01404 (2021)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512186"},{"key":"e_1_3_2_1_28_1","volume-title":"Is Homophily a Necessity for Graph Neural Networks? arXiv preprint arXiv:2106.06134","author":"Ma Yao","year":"2021","unstructured":"Yao Ma , Xiaorui Liu , Neil Shah , and Jiliang Tang . 2021. Is Homophily a Necessity for Graph Neural Networks? arXiv preprint arXiv:2106.06134 ( 2021 ). Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. 2021. Is Homophily a Necessity for Graph Neural Networks? arXiv preprint arXiv:2106.06134 (2021)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1954.1057469"},{"key":"e_1_3_2_1_30_1","volume-title":"f-gan: Training generative neural samplers using variational divergence minimization. Advances in neural information processing systems","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin , Botond Cseke , and Ryota Tomioka . 2016. f-gan: Training generative neural samplers using variational divergence minimization. Advances in neural information processing systems , Vol. 29 ( 2016 ). Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-gan: Training generative neural samplers using variational divergence minimization. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_1_31_1","volume-title":"Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Pei Hongbin","year":"2019","unstructured":"Hongbin Pei , Bingzhe Wei , Kevin Chen-Chuan Chang , Yu Lei , and Bo Yang . 2019 . Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations. Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2019. Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_32_1","volume-title":"Yu Lei, and Bo Yang.","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. arXiv preprint arXiv:2002.05287 (2020). Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287 (2020)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098061"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_1_35_1","volume-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000","author":"Sun Fan-Yun","year":"2019","unstructured":"Fan-Yun Sun , Jordan Hoffmann , Vikas Verma , and Jian Tang . 2019 . Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019). Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"e_1_3_2_1_38_1","volume-title":"ICLR 2021 Workshop on Geometrical and Topological Representation Learning.","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor , Corentin Tallec , Mohammad Gheshlaghi Azar , R\u00e9mi Munos , Petar Velivc kovi\u0107, and Michal Valko . 2021 . Bootstrapped representation learning on graphs . In ICLR 2021 Workshop on Geometrical and Topological Representation Learning. Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, R\u00e9mi Munos, Petar Velivc kovi\u0107, and Michal Valko. 2021. Bootstrapped representation learning on graphs. In ICLR 2021 Workshop on Geometrical and Topological Representation Learning."},{"key":"e_1_3_2_1_39_1","first-page":"6827","article-title":"What makes for good views for contrastive learning","volume":"33","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian , Chen Sun , Ben Poole , Dilip Krishnan , Cordelia Schmid , and Phillip Isola . 2020 . What makes for good views for contrastive learning ? Advances in Neural Information Processing Systems , Vol. 33 (2020), 6827 -- 6839 . Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020. What makes for good views for contrastive learning? Advances in Neural Information Processing Systems, Vol. 33 (2020), 6827--6839.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512200"},{"key":"e_1_3_2_1_41_1","volume-title":"Representation learning with contrastive predictive coding. arXiv e-prints","author":"den Oord Aaron Van","year":"2018","unstructured":"Aaron Van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv e-prints ( 2018 ), arXiv--1807. Aaron Van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv e-prints (2018), arXiv--1807."},{"key":"e_1_3_2_1_42_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_43_1","volume-title":"Graph Attention Networks. International Conference on Learning Representations","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=rJXMpikCZ accepted as poster. Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=rJXMpikCZ accepted as poster."},{"key":"e_1_3_2_1_44_1","first-page":"4","article-title":"Deep Graph Infomax","volume":"2","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic , William Fedus , William L Hamilton , Pietro Li\u00f2 , Yoshua Bengio , and R Devon Hjelm . 2019 . Deep Graph Infomax . ICLR (Poster) , Vol. 2 , 3 (2019), 4 . Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. ICLR (Poster), Vol. 2, 3 (2019), 4.","journal-title":"ICLR (Poster)"},{"key":"e_1_3_2_1_45_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_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449952"},{"key":"e_1_3_2_1_47_1","volume-title":"Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH","author":"Marinka Zitnik Percy Liang Joseph Gomes","year":"2020","unstructured":"Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande Jure Leskovec Weihua Hu , Bowen Liu . 2020 . Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande Jure Leskovec Weihua Hu, Bowen Liu. 2020. Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH"},{"key":"e_1_3_2_1_48_1","volume-title":"Self-supervised learning on graphs: Contrastive, generative, or predictive","author":"Wu Lirong","year":"2021","unstructured":"Lirong Wu , Haitao Lin , Cheng Tan , Zhangyang Gao , and Stan Z Li. 2021. Self-supervised learning on graphs: Contrastive, generative, or predictive . IEEE Transactions on Knowledge and Data Engineering ( 2021 ). Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z Li. 2021. Self-supervised learning on graphs: Contrastive, generative, or predictive. IEEE Transactions on Knowledge and Data Engineering (2021)."},{"key":"e_1_3_2_1_49_1","volume-title":"Self-supervised learning of graph neural networks: A unified review","author":"Xie Yaochen","year":"2022","unstructured":"Yaochen Xie , Zhao Xu , Jingtun Zhang , Zhengyang Wang , and Shuiwang Ji. 2022. Self-supervised learning of graph neural networks: A unified review . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2022 ). Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji. 2022. Self-supervised learning of graph neural networks: A unified review. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_1_50_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km","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. https:\/\/openreview.net\/forum?id=ryGs6iA5Km Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"e_1_3_2_1_51_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 . 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_52_1","volume-title":"Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. arXiv preprint arXiv:2102.06462","author":"Yan Yujun","year":"2021","unstructured":"Yujun Yan , Milad Hashemi , Kevin Swersky , Yaoqing Yang , and Danai Koutra . 2021. Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. arXiv preprint arXiv:2102.06462 ( 2021 ). Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. 2021. Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. arXiv preprint arXiv:2102.06462 (2021)."},{"key":"e_1_3_2_1_53_1","volume-title":"Rethinking graph regularization for graph neural networks. arXiv preprint arXiv:2009.02027","author":"Yang Han","year":"2020","unstructured":"Han Yang , Kaili Ma , and James Cheng . 2020. Rethinking graph regularization for graph neural networks. arXiv preprint arXiv:2009.02027 ( 2020 ). Han Yang, Kaili Ma, and James Cheng. 2020. Rethinking graph regularization for graph neural networks. arXiv preprint arXiv:2009.02027 (2020)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16588"},{"key":"e_1_3_2_1_55_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","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 . Advances in Neural Information Processing Systems , Vol. 33 (2020), 5812 -- 5823 . Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5812--5823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_56_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Zhang Hengrui","year":"2021","unstructured":"Hengrui Zhang , Qitian Wu , Junchi Yan , David Wipf , and Philip S Yu . 2021 . From canonical correlation analysis to self-supervised graph neural networks . Advances in Neural Information Processing Systems , Vol. 34 (2021). Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S Yu. 2021. From canonical correlation analysis to self-supervised graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_1_57_1","volume-title":"Attributed graph clustering via adaptive graph convolution. arXiv preprint arXiv:1906.01210","author":"Zhang Xiaotong","year":"2019","unstructured":"Xiaotong Zhang , Han Liu , Qimai Li , and Xiao-Ming Wu. 2019. Attributed graph clustering via adaptive graph convolution. arXiv preprint arXiv:1906.01210 ( 2019 ). Xiaotong Zhang, Han Liu, Qimai Li, and Xiao-Ming Wu. 2019. Attributed graph clustering via adaptive graph convolution. arXiv preprint arXiv:1906.01210 (2019)."},{"key":"e_1_3_2_1_58_1","volume-title":"Graph Neural Networks for Graphs with Heterophily: A Survey. arXiv preprint arXiv:2202.07082","author":"Zheng Xin","year":"2022","unstructured":"Xin Zheng , Yixin Liu , Shirui Pan , Miao Zhang , Di Jin , and Philip S Yu. 2022. Graph Neural Networks for Graphs with Heterophily: A Survey. arXiv preprint arXiv:2202.07082 ( 2022 ). Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu. 2022. Graph Neural Networks for Graphs with Heterophily: A Survey. arXiv preprint arXiv:2202.07082 (2022)."},{"key":"e_1_3_2_1_59_1","volume-title":"Unsupervised Heterophilous Network Embedding via $ r $-Ego Network Discrimination. arXiv preprint arXiv:2203.10866","author":"Zhong Zhiqiang","year":"2022","unstructured":"Zhiqiang Zhong , Guadalupe Gonzalez , Daniele Grattarola , and Jun Pang . 2022. Unsupervised Heterophilous Network Embedding via $ r $-Ego Network Discrimination. arXiv preprint arXiv:2203.10866 ( 2022 ). Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola, and Jun Pang. 2022. Unsupervised Heterophilous Network Embedding via $ r $-Ego Network Discrimination. arXiv preprint arXiv:2203.10866 (2022)."},{"key":"e_1_3_2_1_60_1","volume-title":"On the Relationship between Heterophily and Robustness of Graph Neural Networks. arXiv preprint arXiv:2106.07767","author":"Zhu Jiong","year":"2021","unstructured":"Jiong Zhu , Junchen Jin , Donald Loveland , Michael T Schaub , and Danai Koutra . 2021a. On the Relationship between Heterophily and Robustness of Graph Neural Networks. arXiv preprint arXiv:2106.07767 ( 2021 ). Jiong Zhu, Junchen Jin, Donald Loveland, Michael T Schaub, and Danai Koutra. 2021a. On the Relationship between Heterophily and Robustness of Graph Neural Networks. arXiv preprint arXiv:2106.07767 (2021)."},{"key":"e_1_3_2_1_61_1","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020 b. Beyond homophily in graph neural networks: Current limitations and effective designs . Advances in Neural Information Processing Systems , Vol. 33 (2020), 7793 -- 7804 . Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7793--7804.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"},{"key":"e_1_3_2_1_63_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 . 2020a. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 ( 2020 ). Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020a. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557478","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557478","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:56Z","timestamp":1750182536000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":64,"alternative-id":["10.1145\/3511808.3557478","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557478","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}