{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:24:58Z","timestamp":1780601098559,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":50,"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":"Science and Technology Development Program of Jilin Province award number(s)","award":["No. 20210508060RQ"],"award-info":[{"award-number":["No. 20210508060RQ"]}]},{"name":"the Foundation of the Major Project of Science and Technology Innovation 2030 - New Generation of Artificial Intelligence award number(s)","award":["No.2021ZD0112500"],"award-info":[{"award-number":["No.2021ZD0112500"]}]},{"name":"the Interdisciplinary and integrated innovation of JLU award number(s)","award":["No. JLUXKJC2020207"],"award-info":[{"award-number":["No. JLUXKJC2020207"]}]},{"name":"the National Natural Science Foundation of China award number(s)","award":["No. 61872161, No. 61976102"],"award-info":[{"award-number":["No. 61872161, No. 61976102"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557228","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"2046-2055","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["AdaGCL"],"prefix":"10.1145","author":[{"given":"Yili","family":"Wang","sequence":"first","affiliation":[{"name":"Jilin University, Changchun, UNK, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixiong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Rice University, Houston, UNK, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Miao","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, UNK, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ninghao","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, UNK, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, UNK, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Graph Barlow Twins: A self-supervised representation learning framework for graphs. arXiv preprint arXiv:2106.02466","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 preprint arXiv:2106.02466 ( 2021 ). Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V Chawla. 2021. Graph Barlow Twins: A self-supervised representation learning framework for graphs. arXiv preprint arXiv:2106.02466 (2021)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_2_3_1","volume-title":"Fastgcn: fast learning with graph convolutional networks via importance sampling. 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:1801.10247 ( 2018 ). Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv:1801.10247 (2018)."},{"key":"e_1_3_2_2_4_1","volume-title":"Stochastic training of graph convolu- tional networks with variance reduction. arXiv:1710.10568","author":"Chen Jianfei","year":"2017","unstructured":"Jianfei Chen , Jun Zhu , and Le Song . 2017. Stochastic training of graph convolu- tional networks with variance reduction. arXiv:1710.10568 ( 2017 ). Jianfei Chen, Jun Zhu, and Le Song. 2017. Stochastic training of graph convolu- tional networks with variance reduction. arXiv:1710.10568 (2017)."},{"key":"e_1_3_2_2_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_2_6_1","volume-title":"Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study","author":"Chen Tianlong","year":"2022","unstructured":"Tianlong Chen , Kaixiong Zhou , Keyu Duan , Wenqing Zheng , Peihao Wang , Xia Hu , and Zhangyang Wang . 2022. Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2022 ). Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, and Zhangyang Wang. 2022. Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824077"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"e_1_3_2_2_10_1","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"33","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill , Florian Strub , Florent Altch\u00e9 , Corentin Tallec , Pierre Richemond , Elena Buchatskaya , Carl Doersch , Bernardo Avila Pires , Zhaohan Guo , Mohammad Gheshlaghi Azar , 2020 . Bootstrap your own latent-a new approach to self-supervised learning . Advances in Neural Information Processing Systems 33 (2020), 21271 -- 21284 . Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems 33 (2020), 21271--21284.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20316"},{"key":"e_1_3_2_2_12_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems 30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton , Zhitao Ying , and Jure Leskovec . 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30 ( 2017 ). Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_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 rep- resentation learning on graphs . In International Conference on Machine Learning. PMLR, 4116--4126 . Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view rep- resentation learning on graphs. In International Conference on Machine Learning. PMLR, 4116--4126."},{"key":"e_1_3_2_2_14_1","volume-title":"Learning Graph Augmentations to Learn Graph Representations. arXiv preprint arXiv:2201.09830","author":"Hassani Kaveh","year":"2022","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi . 2022. Learning Graph Augmentations to Learn Graph Representations. arXiv preprint arXiv:2201.09830 ( 2022 ). Kaveh Hassani and Amir Hosein Khasahmadi. 2022. Learning Graph Augmentations to Learn Graph Representations. arXiv preprint arXiv:2201.09830 (2022)."},{"key":"e_1_3_2_2_15_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems 33","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Matthias Fey , Marinka Zitnik , Yuxiao Dong , Hongyu Ren , Bowen Liu , Michele Catasta , and Jure Leskovec . 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems 33 ( 2020 ), 22118--22133. 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 33 (2020), 22118--22133."},{"key":"e_1_3_2_2_16_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 ). 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_2_17_1","volume-title":"International Conference on Learning Representations.","author":"Liu Zirui","year":"2021","unstructured":"Zirui Liu , Kaixiong Zhou , Fan Yang , Li Li , Rui Chen , and Xia Hu . 2021 . EXACT: Scalable graph neural networks training via extreme activation compression . In International Conference on Learning Representations. Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, and Xia Hu. 2021. EXACT: Scalable graph neural networks training via extreme activation compression. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_2_19_1","volume-title":"Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903","author":"Rong Yu","year":"2019","unstructured":"Yu Rong , Wenbing Huang , Tingyang Xu , and Junzhou Huang . 2019 . Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019). Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019)."},{"key":"e_1_3_2_2_20_1","volume-title":"Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi , Fabrizio Frasca , Ben Chamberlain , Davide Eynard , Michael Bronstein , and Federico Monti . 2020 . Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198 (2020). Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti. 2020. Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198 (2020)."},{"key":"e_1_3_2_2_21_1","volume-title":"Infograph: Un- supervised 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: Un- supervised 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: Un- supervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_2_2_23_1","unstructured":"Susheel Suresh Pan Li Cong Hao and Jennifer Neville. 2021. Adversarial Graph Augmentation to Improve Graph Contrastive Learning. arXiv:2106.05819 [cs.LG]  Susheel Suresh Pan Li Cong Hao and Jennifer Neville. 2021. Adversarial Graph Augmentation to Improve Graph Contrastive Learning. arXiv:2106.05819 [cs.LG]"},{"key":"e_1_3_2_2_24_1","volume-title":"R\u00e9mi Munos, Petar Veli\u010dkovi\u010d, and Michal Valko.","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor , Corentin Tallec , Mohammad Gheshlaghi Azar , R\u00e9mi Munos, Petar Veli\u010dkovi\u010d, and Michal Valko. 2021 . Bootstrapped representation learning on graphs. arXiv preprint arXiv:2102.06514 (2021). Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, R\u00e9mi Munos, Petar Veli\u010dkovi\u010d, and Michal Valko. 2021. Bootstrapped representation learning on graphs. arXiv preprint arXiv:2102.06514 (2021)."},{"key":"e_1_3_2_2_25_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 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 33 (2020), 6827--6839.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_26_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_2_27_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_28_1","first-page":"4","article-title":"Deep Graph Infomax","volume":"2","author":"Veli\u010dkovi\u0107 Petar","year":"2019","unstructured":"Petar Veli\u010dkovi\u0107 , William Fedus , William L Hamilton , Pietro Li\u00f2 , Yoshua Bengio , and R Devon Hjelm . 2019 . Deep Graph Infomax . ICLR (Poster) 2 , 3 (2019), 4 . Petar Veli\u010dkovi\u0107, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. ICLR (Poster) 2, 3 (2019), 4.","journal-title":"ICLR (Poster)"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269252"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6095"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886580"},{"key":"e_1_3_2_2_33_1","first-page":"2958","article-title":"Adversarial weight perturbation helps robust generalization","volume":"33","author":"Wu Dongxian","year":"2020","unstructured":"Dongxian Wu , Shu-Tao Xia , and Yisen Wang . 2020 . Adversarial weight perturbation helps robust generalization . Advances in Neural Information Processing Systems 33 (2020), 2958 -- 2969 . Dongxian Wu, Shu-Tao Xia, and Yisen Wang. 2020. Adversarial weight perturbation helps robust generalization. Advances in Neural Information Processing Systems 33 (2020), 2958--2969.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_34_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 . 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_2_35_1","volume-title":"Infogcl: Information-aware graph contrastive learning. Advances in Neural Information Processing Systems 34","author":"Xu Dongkuan","year":"2021","unstructured":"Dongkuan Xu , Wei Cheng , Dongsheng Luo , Haifeng Chen , and Xiang Zhang . 2021 . Infogcl: Information-aware graph contrastive learning. Advances in Neural Information Processing Systems 34 (2021). Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, and Xiang Zhang. 2021. Infogcl: Information-aware graph contrastive learning. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_36_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks? arXiv:1810.00826 [cs.LG]  Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks? arXiv:1810.00826 [cs.LG]"},{"key":"e_1_3_2_2_37_1","volume-title":"CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks. arXiv preprint arXiv:2110.14855","author":"Xue Haotian","year":"2021","unstructured":"Haotian Xue , Kaixiong Zhou , Tianlong Chen , Kai Guo , Xia Hu , Yi Chang , and Xin Wang . 2021 . CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks. arXiv preprint arXiv:2110.14855 (2021). Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, and Xin Wang. 2021. CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks. arXiv preprint arXiv:2110.14855 (2021)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102946"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Machine Learning. PMLR, 12121--12132","author":"You Yuning","year":"2021","unstructured":"Yuning You , Tianlong Chen , Yang Shen , and Zhangyang Wang . 2021 . Graph contrastive learning automated . In International Conference on Machine Learning. PMLR, 12121--12132 . Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. In International Conference on Machine Learning. PMLR, 12121--12132."},{"key":"e_1_3_2_2_41_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 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 33 (2020), 5812--5823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Machine Learning. PMLR, 12310--12320","author":"Zbontar Jure","year":"2021","unstructured":"Jure Zbontar , Li Jing , Ishan Misra , Yann LeCun , and St\u00e9phane Deny . 2021 . Barlow twins: Self-supervised learning via redundancy reduction . In International Conference on Machine Learning. PMLR, 12310--12320 . Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and St\u00e9phane Deny. 2021. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning. PMLR, 12310--12320."},{"key":"e_1_3_2_2_43_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). 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_2_44_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/473"},{"key":"e_1_3_2_2_45_1","first-page":"4917","article-title":"Towards deeper graph neural networks with differentiable group normalization","volume":"33","author":"Zhou Kaixiong","year":"2020","unstructured":"Kaixiong Zhou , Xiao Huang , Yuening Li , Daochen Zha , Rui Chen , and Xia Hu . 2020 . Towards deeper graph neural networks with differentiable group normalization . Advances in Neural Information Processing Systems 33 (2020), 4917 -- 4928 . Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. Advances in Neural Information Processing Systems 33 (2020), 4917--4928.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_46_1","volume-title":"Dirichlet energy constrained learning for deep graph neural networks. Advances in Neural Information Processing Systems 34","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou , Xiao Huang , Daochen Zha , Rui Chen , Li Li , Soo-Hyun Choi , and Xia Hu. 2021. Dirichlet energy constrained learning for deep graph neural networks. Advances in Neural Information Processing Systems 34 ( 2021 ). Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu. 2021. Dirichlet energy constrained learning for deep graph neural networks. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_47_1","volume-title":"Adaptive Label Smoothing To Regularize Large-Scale Graph Training. arXiv preprint arXiv:2108.13555","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou , Ninghao Liu , Fan Yang , Zirui Liu , Rui Chen , Li Li , Soo-Hyun Choi , and Xia Hu. 2021. Adaptive Label Smoothing To Regularize Large-Scale Graph Training. arXiv preprint arXiv:2108.13555 ( 2021 ). Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu. 2021. Adaptive Label Smoothing To Regularize Large-Scale Graph Training. arXiv preprint arXiv:2108.13555 (2021)."},{"key":"e_1_3_2_2_48_1","volume-title":"Auto-gnn: Neural architecture search of graph neural networks. arXiv:1909.03184","author":"Zhou Kaixiong","year":"2019","unstructured":"Kaixiong Zhou , Qingquan Song , Xiao Huang , and Xia Hu . 2019 . Auto-gnn: Neural architecture search of graph neural networks. arXiv:1909.03184 (2019). Kaixiong Zhou, Qingquan Song, Xiao Huang, and Xia Hu. 2019. Auto-gnn: Neural architecture search of graph neural networks. arXiv:1909.03184 (2019)."},{"key":"e_1_3_2_2_49_1","volume-title":"Proceedings of the Twenty- Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou , Qingquan Song , Xiao Huang , Daochen Zha , Na Zou , and Xia Hu . 2021 . Multi-channel graph neural networks . In Proceedings of the Twenty- Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358 . Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. 2021. Multi-channel graph neural networks. In Proceedings of the Twenty- Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358."},{"key":"e_1_3_2_2_50_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 &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557228","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557228","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:07Z","timestamp":1750182547000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557228"}},"subtitle":["Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training"],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":50,"alternative-id":["10.1145\/3511808.3557228","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557228","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"}}]}}