{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T18:57:55Z","timestamp":1778266675301,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2018AAA0101900"],"award-info":[{"award-number":["2018AAA0101900"]}]},{"name":"Zhejiang NSF","award":["LR22F020005"],"award-info":[{"award-number":["LR22F020005"]}]},{"name":"Fundamental Research Funds for the Central Universities"},{"name":"NSFC","award":["62206056"],"award-info":[{"award-number":["62206056"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599548","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"142-153","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["When to Pre-Train Graph Neural Networks? From Data Generation Perspective!"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2867-8938","authenticated-orcid":false,"given":"Yuxuan","family":"Cao","sequence":"first","affiliation":[{"name":"Zhejiang University &amp; Fudan University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2973-1889","authenticated-orcid":false,"given":"Jiarong","family":"Xu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9145-4531","authenticated-orcid":false,"given":"Carl","family":"Yang","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2587-7648","authenticated-orcid":false,"given":"Jiaan","family":"Wang","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3226-5324","authenticated-orcid":false,"given":"Yunchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3841-1919","authenticated-orcid":false,"given":"Chunping","family":"Wang","sequence":"additional","affiliation":[{"name":"Finvolution Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4912-3293","authenticated-orcid":false,"given":"Lei","family":"CHEN","sequence":"additional","affiliation":[{"name":"Finvolution Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5058-4417","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Edo M Airoldi Thiago B Costa and Stanley H Chan. 2013. Stochastic blockmodel approximation of a graphon: Theory and consistent estimation. In NeurIPS. 692--700. Edo M Airoldi Thiago B Costa and Stanley H Chan. 2013. Stochastic blockmodel approximation of a graphon: Theory and consistent estimation. In NeurIPS. 692--700."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Katy B\u00f6rner Soma Sanyal Alessandro Vespignani etal 2007. Network science. Annu. rev. inf. sci. technol. Vol. 41 1 (2007) 537--607. Katy B\u00f6rner Soma Sanyal Alessandro Vespignani et al. 2007. Network science. Annu. rev. inf. sci. technol. Vol. 41 1 (2007) 537--607.","DOI":"10.1002\/aris.2007.1440410119"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-EJS753"},{"key":"e_1_3_2_2_4_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT. 4171--4186.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT. 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT. 4171--4186."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Claire Donnat Marinka Zitnik David Hallac and Jure Leskovec. 2018. Learning Structural Node Embeddings via Diffusion Wavelets. In SIGKDD. Claire Donnat Marinka Zitnik David Hallac and Jure Leskovec. 2018. Learning Structural Node Embeddings via Diffusion Wavelets. In SIGKDD.","DOI":"10.1145\/3219819.3220025"},{"key":"e_1_3_2_2_6_1","first-page":"238","article-title":"Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology","volume":"1","author":"Freeman Linton C","year":"2002","unstructured":"Linton C Freeman 2002 . Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology . Londres: Routledge , Vol. 1 (2002), 238 -- 263 . Linton C Freeman et al. 2002. Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge, Vol. 1 (2002), 238--263.","journal-title":"Londres: Routledge"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In SIGKDD. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In SIGKDD.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_8_1","volume-title":"GraphCL: Contrastive Self-Supervised Learning of Graph Representations. ArXiv","author":"Hafidi Hakim","year":"2020","unstructured":"Hakim Hafidi , Mounir Ghogho , Philippe Ciblat , and Ananthram Swami . 2020. GraphCL: Contrastive Self-Supervised Learning of Graph Representations. ArXiv , Vol. abs\/ 2007 .08025 ( 2020 ). Hakim Hafidi, Mounir Ghogho, Philippe Ciblat, and Ananthram Swami. 2020. GraphCL: Contrastive Self-Supervised Learning of Graph Representations. ArXiv, Vol. abs\/2007.08025 (2020)."},{"key":"e_1_3_2_2_9_1","unstructured":"Xueting Han Zhenhuan Huang Bang An and Jing Bai. 2021. Adaptive Transfer Learning on Graph Neural Networks. In SIGKDD. Xueting Han Zhenhuan Huang Bang An and Jing Bai. 2021. Adaptive Transfer Learning on Graph Neural Networks. In SIGKDD."},{"key":"e_1_3_2_2_10_1","unstructured":"Xiaotian Han Zhimeng Jiang Ninghao Liu and Xia Hu. 2022. G-Mixup: Graph Data Augmentation for Graph Classification. In ICML. Xiaotian Han Zhimeng Jiang Ninghao Liu and Xia Hu. 2022. G-Mixup: Graph Data Augmentation for Graph Classification. In ICML."},{"key":"e_1_3_2_2_11_1","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In ICML. PMLR 4116--4126. Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In ICML. PMLR 4116--4126."},{"key":"e_1_3_2_2_12_1","unstructured":"Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738. Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Zhenyu Hou Xiao Liu Yukuo Cen Yuxiao Dong Hongxia Yang Chunjie Wang and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In SIGKDD. 594--604. Zhenyu Hou Xiao Liu Yukuo Cen Yuxiao Dong Hongxia Yang Chunjie Wang and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In SIGKDD. 594--604.","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_3_2_2_14_1","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2020b. Strategies for pre-training graph neural networks. In ICLR. Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2020b. Strategies for pre-training graph neural networks. In ICLR."},{"key":"e_1_3_2_2_15_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020a. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD. Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020a. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD."},{"key":"e_1_3_2_2_16_1","volume-title":"Pre-Training Graph Neural Networks for Generic Structural Feature Extraction. ArXiv","author":"Hu Ziniu","year":"2019","unstructured":"Ziniu Hu , Changjun Fan , Ting Chen , Kai-Wei Chang , and Yizhou Sun . 2019. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction. ArXiv , Vol. abs\/ 1905 .13728 ( 2019 ). Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, and Yizhou Sun. 2019. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction. ArXiv, Vol. abs\/1905.13728 (2019)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/10\/8\/083042"},{"key":"e_1_3_2_2_18_1","volume-title":"Variational Graph Auto-Encoders. ArXiv","author":"Kipf Thomas","year":"2016","unstructured":"Thomas Kipf and Max Welling . 2016. Variational Graph Auto-Encoders. ArXiv , Vol. abs\/ 1611 .07308 ( 2016 ). Thomas Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. ArXiv, Vol. abs\/1611.07308 (2016)."},{"key":"e_1_3_2_2_19_1","unstructured":"Pengyong Li Jun Wang Ziliang Li Yixuan Qiao Xianggen Liu Fei Ma Peng Gao Sen Song and Guowang Xie. 2021. Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks. In IJCAI. Pengyong Li Jun Wang Ziliang Li Yixuan Qiao Xianggen Liu Fei Ma Peng Gao Sen Song and Guowang Xie. 2021. Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks. In IJCAI."},{"key":"e_1_3_2_2_20_1","unstructured":"Sihang Li Xiang Wang An Zhang Yingxin Wu Xiangnan He and Tat-Seng Chua. 2022. Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. In ICML. 13052--13065. Sihang Li Xiang Wang An Zhang Yingxin Wu Xiangnan He and Tat-Seng Chua. 2022. Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. In ICML. 13052--13065."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539126"},{"key":"e_1_3_2_2_22_1","volume-title":"Large networks and graph limits","author":"Lov\u00e1sz L\u00e1szl\u00f3","unstructured":"L\u00e1szl\u00f3 Lov\u00e1sz . 2012. Large networks and graph limits . Vol. 60 . American Mathematical Soc . L\u00e1szl\u00f3 Lov\u00e1sz. 2012. Large networks and graph limits. Vol. 60. American Mathematical Soc."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jctb.2006.05.002"},{"key":"e_1_3_2_2_24_1","unstructured":"Yuanfu Lu Xunqiang Jiang Yuan Fang and Chuan Shi. 2021. Learning to Pre-train Graph Neural Networks. In AAAI. Yuanfu Lu Xunqiang Jiang Yuan Fang and Chuan Shi. 2021. Learning to Pre-train Graph Neural Networks. In AAAI."},{"key":"e_1_3_2_2_25_1","volume-title":"5th Berkeley Symp. Math. Statist. Probability. University of California Los Angeles LA USA, 281--297","author":"MacQueen J","year":"1967","unstructured":"J MacQueen . 1967 . Classification and analysis of multivariate observations . In 5th Berkeley Symp. Math. Statist. Probability. University of California Los Angeles LA USA, 281--297 . J MacQueen. 1967. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability. University of California Los Angeles LA USA, 281--297."},{"key":"e_1_3_2_2_26_1","volume-title":"Science","volume":"298","author":"Milo Ron","year":"2002","unstructured":"Ron Milo , Shai Shen-Orr , Shalev Itzkovitz , Nadav Kashtan , Dmitri Chklovskii , and Uri Alon . 2002 . Network motifs: simple building blocks of complex networks . Science , Vol. 298 , 5594 (2002), 824--827. Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon. 2002. Network motifs: simple building blocks of complex networks. Science, Vol. 298, 5594 (2002), 824--827."},{"key":"e_1_3_2_2_27_1","volume-title":"graph2vec: Learning Distributed Representations of Graphs. ArXiv","author":"Narayanan Annamalai","unstructured":"Annamalai Narayanan , Mahinthan Chandramohan , Rajasekar Venkatesan , Lihui Chen , Yang Liu , and Shantanu Jaiswal . 2017. graph2vec: Learning Distributed Representations of Graphs. ArXiv , Vol. abs\/ 1707 .05005. Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. graph2vec: Learning Distributed Representations of Graphs. ArXiv, Vol. abs\/1707.05005."},{"key":"e_1_3_2_2_28_1","volume-title":"Mixing patterns in networks. Physical review E","author":"Newman Mark EJ","year":"2003","unstructured":"Mark EJ Newman . 2003. Mixing patterns in networks. Physical review E , Vol. 67 , 2 ( 2003 ), 026126. Mark EJ Newman. 2003. Mixing patterns in networks. Physical review E, Vol. 67, 2 (2003), 026126."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi Rami Al-Rfou and Steven Skiena. 2014. DeepWalk: online learning of social representations. In SIGKDD. Bryan Perozzi Rami Al-Rfou and Steven Skiena. 2014. DeepWalk: online learning of social representations. In SIGKDD.","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_30_1","unstructured":"Gabriel Peyr\u00e9 Marco Cuturi and Justin Solomon. 2016. Gromov-wasserstein averaging of kernel and distance matrices. In ICML. PMLR 2664--2672. Gabriel Peyr\u00e9 Marco Cuturi and Justin Solomon. 2016. Gromov-wasserstein averaging of kernel and distance matrices. In ICML. PMLR 2664--2672."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00559"},{"key":"e_1_3_2_2_33_1","unstructured":"Fan-Yun Sun Jordan Hoffmann and Jian Tang. 2020a. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR. Fan-Yun Sun Jordan Hoffmann and Jian Tang. 2020a. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Ke Sun Zhanxing Zhu and Zhouchen Lin. 2020b. Multi-Stage Self-Supervised Learning for Graph Convolutional Networks. In AAAI. Ke Sun Zhanxing Zhu and Zhouchen Lin. 2020b. Multi-Stage Self-Supervised Learning for Graph Convolutional Networks. In AAAI.","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"e_1_3_2_2_35_1","unstructured":"Mengying Sun Jing Xing Huijun Wang Bin Chen and Jiayu Zhou. 2021. MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph. In SIGKDD. Mengying Sun Jing Xing Huijun Wang Bin Chen and Jiayu Zhou. 2021. MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph. In SIGKDD."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_37_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_38_1","doi-asserted-by":"crossref","unstructured":"Stanley Wasserman and Katherine Faust. 1994. Social network analysis: Methods and applications. (1994). Stanley Wasserman and Katherine Faust. 1994. Social network analysis: Methods and applications. (1994).","DOI":"10.1017\/CBO9780511815478"},{"key":"e_1_3_2_2_39_1","unstructured":"Max Welling and Thomas N Kipf. 2016. Semi-supervised classification with graph convolutional networks. In ICLR. Max Welling and Thomas N Kipf. 2016. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_40_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu , Bharath Ramsundar , Evan N Feinberg , Joseph Gomes , Caleb Geniesse , Aneesh S Pappu , Karl Leswing , and Vijay Pande . 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science , Vol. 9 , 2 ( 2018 ), 513--530. Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science, Vol. 9, 2 (2018), 513--530."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2019.2952908"},{"key":"e_1_3_2_2_42_1","volume-title":"Li","author":"Xia Jun","year":"2022","unstructured":"Jun Xia , Jiangbin Zheng , Cheng Tan , Ge Wang , and Stan Z . Li . 2022 . Towards Effective and Generalizable Fine-tuning for Pre-trained Molecular Graph Models . bioRxiv (2022). Jun Xia, Jiangbin Zheng, Cheng Tan, Ge Wang, and Stan Z. Li. 2022. Towards Effective and Generalizable Fine-tuning for Pre-trained Molecular Graph Models. bioRxiv (2022)."},{"key":"e_1_3_2_2_43_1","volume-title":"Learning Graphon Autoencoders for Generative Graph Modeling. ArXiv","author":"Xu Hongteng","year":"2021","unstructured":"Hongteng Xu , Peilin Zhao , Junzhou Huang , and Dixin Luo . 2021. Learning Graphon Autoencoders for Generative Graph Modeling. ArXiv , Vol. abs\/ 2105 .14244 ( 2021 ). Hongteng Xu, Peilin Zhao, Junzhou Huang, and Dixin Luo. 2021. Learning Graphon Autoencoders for Generative Graph Modeling. ArXiv, Vol. abs\/2105.14244 (2021)."},{"key":"e_1_3_2_2_44_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR. Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR."},{"key":"e_1_3_2_2_45_1","unstructured":"Yuning You Tianlong Chen Yang Shen and Zhangyang Wang. 2021. Graph contrastive learning automated. In ICML. PMLR 12121--12132. Yuning You Tianlong Chen Yang Shen and Zhangyang Wang. 2021. Graph contrastive learning automated. In ICML. PMLR 12121--12132."},{"key":"e_1_3_2_2_46_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 a. Graph contrastive learning with augmentations . In NeurIPS , Vol. 33. 5812 -- 5823 . Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020a. Graph contrastive learning with augmentations. In NeurIPS, Vol. 33. 5812--5823.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_47_1","first-page":"10871","article-title":"When Does Self-Supervision Help Graph Convolutional Networks?","volume":"119","author":"You Yuning","year":"2020","unstructured":"Yuning You , Tianlong Chen , Zhangyang Wang , and Yang Shen . 2020 b. When Does Self-Supervision Help Graph Convolutional Networks? . In PMLR , Vol. 119. 10871 -- 10880 . Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. 2020b. When Does Self-Supervision Help Graph Convolutional Networks?. In PMLR, Vol. 119. 10871--10880.","journal-title":"PMLR"},{"key":"e_1_3_2_2_48_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. Jie Zhang Yuxiao Dong Yan Wang Jie Tang and Ming Ding. 2019. ProNE: Fast and Scalable Network Representation Learning. In IJCAI.","DOI":"10.24963\/ijcai.2019\/594"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Jiying Zhang Xi Xiao Long-Kai Huang Yu Rong and Yatao Bian. 2022. Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport. In IJCAI. Jiying Zhang Xi Xiao Long-Kai Huang Yu Rong and Yatao Bian. 2022. Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport. In IJCAI.","DOI":"10.24963\/ijcai.2022\/518"},{"key":"e_1_3_2_2_50_1","unstructured":"Zaixin Zhang Qi Liu Hao Wang Chengqiang Lu and Chee-Kong Lee. 2021a. Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. In NeurIPS. Zaixin Zhang Qi Liu Hao Wang Chengqiang Lu and Chee-Kong Lee. 2021a. Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. In NeurIPS."},{"key":"e_1_3_2_2_51_1","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang Zaixi","year":"2021","unstructured":"Zaixi Zhang , Qi Liu , Hao Wang , Chengqiang Lu , and Chee-Kong Lee . 2021 b. Motif-based graph self-supervised learning for molecular property prediction . In NeurIPS , Vol. 34. 15870 -- 15882 . Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. 2021b. Motif-based graph self-supervised learning for molecular property prediction. In NeurIPS, Vol. 34. 15870--15882.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Tong Zhao Yozen Liu Leonardo Neves Oliver J. Woodford Meng Jiang and Neil Shah. 2021. Data Augmentation for Graph Neural Networks. In AAAI. Tong Zhao Yozen Liu Leonardo Neves Oliver J. Woodford Meng Jiang and Neil Shah. 2021. Data Augmentation for Graph Neural Networks. In AAAI.","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_3_2_2_53_1","unstructured":"Qi Zhu Yidan Xu Haonan Wang Chao Zhang Jiawei Han and Carl Yang. 2021. Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization. In NeurIPS. Qi Zhu Yidan Xu Haonan Wang Chao Zhang Jiawei Han and Carl Yang. 2021. Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization. In NeurIPS."},{"key":"e_1_3_2_2_54_1","volume-title":"Deep Graph Contrastive Representation Learning. ArXiv","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu , Yichen Xu , Feng Yu , Q. Liu , Shu Wu , and Liang Wang . 2020. Deep Graph Contrastive Representation Learning. ArXiv , Vol. abs\/ 2006 .04131 ( 2020 ). Yanqiao Zhu, Yichen Xu, Feng Yu, Q. Liu, Shu Wu, and Liang Wang. 2020. Deep Graph Contrastive Representation Learning. ArXiv, Vol. abs\/2006.04131 (2020)."}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599548","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599548","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:52Z","timestamp":1750178272000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599548"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":54,"alternative-id":["10.1145\/3580305.3599548","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599548","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}