{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T02:08:46Z","timestamp":1777342126250,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645604","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"850-860","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Graph Out-of-Distribution Generalization via Causal Intervention"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7734-5945","authenticated-orcid":false,"given":"Qitian","family":"Wu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8822-7448","authenticated-orcid":false,"given":"Fan","family":"Nie","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4435-8284","authenticated-orcid":false,"given":"Chenxiao","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4211-5670","authenticated-orcid":false,"given":"Tianyi","family":"Bao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9639-7679","authenticated-orcid":false,"given":"Junchi","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Invariant risk minimization. arXiv preprint arXiv:1907.02893","author":"Arjovsky Martin","year":"2019","unstructured":"Martin Arjovsky, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts. arXiv preprint arXiv:2302.13875","author":"Bazhenov Gleb","year":"2023","unstructured":"Gleb Bazhenov, Denis Kuznedelev, Andrey Malinin, Artem Babenko, and Liudmila Prokhorenkova. 2023. Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts. arXiv preprint arXiv:2302.13875 (2023)."},{"key":"e_1_3_2_2_3_1","volume-title":"Size-Invariant Graph Representations for Graph Classification Extrapolations. In International Conference on Machine Learning (ICML). 837--851","author":"Bevilacqua Beatrice","year":"2021","unstructured":"Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro. 2021. Size-Invariant Graph Representations for Graph Classification Extrapolations. In International Conference on Machine Learning (ICML). 837--851."},{"key":"e_1_3_2_2_4_1","unstructured":"Davide Buffelli Pietro Li\u00f3 and Fabio Vandin. 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. In NeurIPS."},{"key":"e_1_3_2_2_5_1","volume-title":"causality and robustness. CoRR","author":"B\u00fchlmann Peter","year":"2018","unstructured":"Peter B\u00fchlmann. 2018. Invariance, causality and robustness. CoRR, Vol. abs\/1812.08233 (2018)."},{"key":"e_1_3_2_2_6_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In ICML. 1725--1735."},{"key":"e_1_3_2_2_7_1","volume-title":"Generalizing Graph Neural Networks on Out-Of-Distribution Graphs. arXiv preprint arXiv:2111.10657","author":"Fan Shaohua","year":"2021","unstructured":"Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, and Bai Wang. 2021. Generalizing Graph Neural Networks on Out-Of-Distribution Graphs. arXiv preprint arXiv:2111.10657 (2021)."},{"key":"e_1_3_2_2_8_1","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francc ois Laviolette, Mario Marchand, and Victor S. Lempitsky. 2016. Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res., Vol. 17 (2016), 59:1--59:35.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_9_1","volume-title":"Domain Adaptation with Conditional Transferable Components. In International Conference on Machine Learning (ICML). 2839--2848","author":"Gong Mingming","year":"2016","unstructured":"Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Sch\u00f6 lkopf. 2016. Domain Adaptation with Conditional Transferable Components. In International Conference on Machine Learning (ICML). 2839--2848."},{"key":"e_1_3_2_2_10_1","first-page":"729","article-title":"A new model for learning in graph domains","volume":"2","author":"Gori Marco","year":"2005","unstructured":"Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005. A new model for learning in graph domains. IEEE Trans. Neural Networks, Vol. 2, 1 (2005), 729--734.","journal-title":"IEEE Trans. Neural Networks"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Shengnan Guo Youfang Lin Ning Feng Chao Song and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI. 922--929.","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_2_12_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034."},{"key":"e_1_3_2_2_13_1","volume-title":"NeurIPS","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. In NeurIPS 2020, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)."},{"key":"e_1_3_2_2_14_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_2_15_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fc nnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR."},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Machine Learning (ICML). 5637--5664","author":"Koh Pang Wei","year":"2021","unstructured":"Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. 2021. WILDS: A Benchmark of in-the-Wild Distribution Shifts. In International Conference on Machine Learning (ICML). 5637--5664."},{"key":"e_1_3_2_2_17_1","volume-title":"Remi Le Priol, and Aaron Courville","author":"Krueger David","year":"2021","unstructured":"David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. 2021. Out-of-distribution generalization via risk extrapolation (rex). In ICML."},{"key":"e_1_3_2_2_18_1","volume-title":"Ood-gnn: Out-of-distribution generalized graph neural network. TKDE","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022a. Ood-gnn: Out-of-distribution generalized graph neural network. TKDE (2022)."},{"key":"e_1_3_2_2_19_1","unstructured":"Zenan Li Qitian Wu Fan Nie and Junchi Yan. 2022b. GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_20_1","volume-title":"Structural Re-weighting Improves Graph Domain Adaptation. In International Conference on Machine Learning.","author":"Liu Shikun","year":"2023","unstructured":"Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, and Pan Li. 2023 b. Structural Re-weighting Improves Graph Domain Adaptation. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_21_1","volume-title":"FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1548--1558","author":"Liu Yang","year":"2023","unstructured":"Yang Liu, Xiang Ao, Fuli Feng, Yunshan Ma, Kuan Li, Tat-Seng Chua, and Qing He. 2023 a. FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1548--1558."},{"key":"e_1_3_2_2_22_1","first-page":"1048","article-title":"Subgroup generalization and fairness of graph neural networks","volume":"34","author":"Ma Jiaqi","year":"2021","unstructured":"Jiaqi Ma, Junwei Deng, and Qiaozhu Mei. 2021. Subgroup generalization and fairness of graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 1048--1061.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_23_1","unstructured":"Chris J. Maddison Andriy Mnih and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In ICLR."},{"key":"e_1_3_2_2_24_1","volume-title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In AAAI Conference on Artificial Intelligence (AAAI). 5363--5370","author":"Pareja Aldo","unstructured":"Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson. 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In AAAI Conference on Artificial Intelligence (AAAI). 5363--5370."},{"key":"e_1_3_2_2_25_1","volume-title":"Causal inference in statistics: A primer","author":"Pearl Judea","year":"2016","unstructured":"Judea Pearl, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons (2016)."},{"key":"e_1_3_2_2_26_1","article-title":"Invariant Models for Causal Transfer Learning","volume":"19","author":"Rojas-Carulla Mateo","year":"2018","unstructured":"Mateo Rojas-Carulla, Bernhard Sch\u00f6 lkopf, Richard E. Turner, and Jonas Peters. 2018. Invariant Models for Causal Transfer Learning. Journal of Machine Learning Research, Vol. 19 (2018), 36:1--36:34.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_27_1","unstructured":"Benedek Rozemberczki and Rik Sarkar. 2021. Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings. arxiv: 2101.03091 [cs.SI]"},{"key":"e_1_3_2_2_28_1","volume-title":"Tatsunori B Hashimoto, and Percy Liang.","author":"Sagawa Shiori","year":"2019","unstructured":"Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. 2019. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731 (2019)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_2_30_1","volume-title":"Collective classification in network data. AI magazine","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, Vol. 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_31_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Sui Yongduo","year":"2024","unstructured":"Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, and Xiangnan He. 2024. Unleashing the power of graph data augmentation on covariate distribution shift. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Jie Tang Jimeng Sun Chi Wang and Zi Yang. 2009. Social influence analysis in large-scale networks. In KDD. ACM 807--816.","DOI":"10.1145\/1557019.1557108"},{"key":"e_1_3_2_2_34_1","unstructured":"Jakub Tomczak and Max Welling. 2018. VAE with a VampPrior. In AISTATS."},{"key":"e_1_3_2_2_35_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_2_36_1","volume-title":"Weinberger","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019a. Simplifying Graph Convolutional Networks. In ICML, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). 6861--6871."},{"key":"e_1_3_2_2_37_1","volume-title":"Energy-based Out-of-Distribution Detection for Graph Neural Networks. In International Conference on Learning Representations.","author":"Wu Qitian","year":"2023","unstructured":"Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan. 2023 a. Energy-based Out-of-Distribution Detection for Graph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_38_1","volume-title":"Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. In The World Wide Web Conference. 2091--2102","author":"Wu Qitian","year":"2019","unstructured":"Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019b. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. In The World Wide Web Conference. 2091--2102."},{"key":"e_1_3_2_2_39_1","volume-title":"Handling Distribution Shifts on Graphs: An Invariance Perspective. In International Conference on Learning Representations.","author":"Wu Qitian","year":"2022","unstructured":"Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf. 2022a. Handling Distribution Shifts on Graphs: An Invariance Perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_40_1","unstructured":"Qitian Wu Wentao Zhao Zenan Li David P. Wipf and Junchi Yan. 2022b. NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_41_1","unstructured":"Qitian Wu Wentao Zhao Chenxiao Yang Hengrui Zhang Fan Nie Haitian Jiang Yatao Bian and Junchi Yan. 2023 b. SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Learning Representations.","author":"Yang Chenxiao","year":"2023","unstructured":"Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan. 2023 a. Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_43_1","unstructured":"Nianzu Yang Kaipeng Zeng Qitian Wu Xiaosong Jia and Junchi Yan. 2022. Learning Substructure Invariance for Out-of-Distribution Molecular Representations. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_44_1","volume-title":"MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning. In The Web Conference. 4075--4085","author":"Yang Nianzu","year":"2023","unstructured":"Nianzu Yang, Kaipeng Zeng, Qitian Wu, and Junchi Yan. 2023 b. MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning. In The Web Conference. 4075--4085."},{"key":"e_1_3_2_2_45_1","volume-title":"International Conference on Machine Learning (ICML). 11975--11986","author":"Yehudai Gilad","year":"2021","unstructured":"Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, and Haggai Maron. 2021. From Local Structures to Size Generalization in Graph Neural Networks. In International Conference on Machine Learning (ICML). 11975--11986."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330946"},{"key":"e_1_3_2_2_47_1","volume-title":"Mind the Label Shift of Augmentation-based Graph OOD Generalization. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","author":"Yu Junchi","year":"2023","unstructured":"Junchi Yu, Jian Liang, and Ran He. 2023. Mind the Label Shift of Augmentation-based Graph OOD Generalization. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_48_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3119958"},{"key":"e_1_3_2_2_50_1","volume-title":"Shift-robust gnns: Overcoming the limitations of localized graph training data. NeurIPS","author":"Zhu Qi","year":"2021","unstructured":"Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi. 2021. Shift-robust gnns: Overcoming the limitations of localized graph training data. NeurIPS (2021), 27965--27977."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645604","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645604","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:32:14Z","timestamp":1755822734000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645604"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":51,"alternative-id":["10.1145\/3589334.3645604","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645604","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}