{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:07:50Z","timestamp":1783436870399,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":50,"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"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF0901003"],"award-info":[{"award-number":["2021YFF0901003"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645332","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"344-352","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Cooperative Classification and Rationalization for Graph Generalization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5980-6098","authenticated-orcid":false,"given":"Linan","family":"Yue","sequence":"first","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3436-7620","authenticated-orcid":false,"given":"Ye","family":"Liu","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0894-7023","authenticated-orcid":false,"given":"Weibo","family":"Gao","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5085-7841","authenticated-orcid":false,"given":"Fangzhou","family":"Yao","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1277-8028","authenticated-orcid":false,"given":"Wenfeng","family":"Li","sequence":"additional","affiliation":[{"name":"Douyin Vision Co., Ltd, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Deep Variational Information Bottleneck. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings.","author":"Alemi Alexander A.","year":"2017","unstructured":"Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings."},{"key":"e_1_3_2_2_2_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_3_1","volume-title":"International Conference on Machine Learning. PMLR, 531--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 International Conference on Machine Learning. PMLR, 531--540."},{"key":"e_1_3_2_2_4_1","volume-title":"International Conference on Machine Learning. PMLR, 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. PMLR, 837--851."},{"key":"e_1_3_2_2_5_1","first-page":"31871","article-title":"Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks","volume":"35","author":"Buffelli Davide","year":"2022","unstructured":"Davide Buffelli, Pietro Li\u00f2, and Fabio Vandin. 2022. Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks. Advances in Neural Information Processing Systems 35 (2022), 31871--31885.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_6_1","volume-title":"Invariant Rationalization. In Proceedings of the 37th International Conference on Machine Learning, (ICML).","author":"Chang Shiyu","unstructured":"Shiyu Chang, Yang Zhang, Mo Yu, and Tommi S. Jaakkola. 2020. Invariant Rationalization. In Proceedings of the 37th International Conference on Machine Learning, (ICML)."},{"key":"e_1_3_2_2_7_1","first-page":"22131","article-title":"Learning causally invariant representations for out-of-distribution generalization on graphs","volume":"35","author":"Chen Yongqiang","year":"2022","unstructured":"Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, MA Kaili, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng. 2022. Learning causally invariant representations for out-of-distribution generalization on graphs. Advances in Neural Information Processing Systems 35 (2022), 22131--22148.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00461"},{"key":"e_1_3_2_2_9_1","unstructured":"Shaohua Fan Xiao Wang Yanhu Mo Chuan Shi and Jian Tang. 2022. Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure. (2022)."},{"key":"e_1_3_2_2_10_1","volume-title":"Generalizing graph neural networks on out-of-distribution graphs","author":"Fan Shaohua","year":"2023","unstructured":"Shaohua Fan, XiaoWang, Chuan Shi, Peng Cui, and BaiWang. 2023. Generalizing graph neural networks on out-of-distribution graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-020-0255-y"},{"key":"e_1_3_2_2_13_1","volume-title":"Graph random neural networks for semi-supervised learning on graphs. Advances in neural information processing systems 33","author":"Feng Wenzheng","year":"2020","unstructured":"Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph random neural networks for semi-supervised learning on graphs. Advances in neural information processing systems 33 (2020), 22092--22103."},{"key":"e_1_3_2_2_14_1","volume-title":"Zheng Zhangm Xin Li, and Yuanjing He","author":"Gao Weibo","year":"2023","unstructured":"Weibo Gao, Qi Liu, Hao Wang, Linan Yue, Haoyang Bi, Yin Gu, Fangzhou Yao, Zheng Zhangm Xin Li, and Yuanjing He. 2023. Zero-1-to-3: Domain-level Zeroshot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives. arXiv preprint arXiv:2312.13434 (2023)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591774"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450112"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"e_1_3_2_2_18_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."},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Learning Representations (ICLR","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical reparametrization with gumble-softmax. In International Conference on Learning Representations (ICLR 2017). OpenReview. net."},{"key":"e_1_3_2_2_20_1","volume-title":"Proceedings of the 3rd ICLR.","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the 3rd ICLR."},{"key":"e_1_3_2_2_21_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)."},{"key":"e_1_3_2_2_22_1","volume-title":"Understanding attention and generalization in graph neural networks. Advances in neural information processing systems 32","author":"Knyazev Boris","year":"2019","unstructured":"Boris Knyazev, Graham W Taylor, and Mohamed Amer. 2019. Understanding attention and generalization in graph neural networks. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_24_1","volume-title":"Ood-gnn: Out-ofdistribution generalized graph neural network","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022. Ood-gnn: Out-ofdistribution generalized graph neural network. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_2_25_1","volume-title":"andWenwu Zhu","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, XinWang, Ziwei Zhang, andWenwu Zhu. 2022. Out-of-distribution generalization on graphs: A survey. arXiv preprint arXiv:2202.07987 (2022)."},{"key":"e_1_3_2_2_26_1","unstructured":"Haoyang Li Ziwei Zhang Xin Wang and Wenwu Zhu. 2022. Learning invariant graph representations for out-of-distribution generalization. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539347"},{"key":"e_1_3_2_2_28_1","unstructured":"Songtao Liu Rex Ying Hanze Dong Lanqing Li Tingyang Xu Yu Rong Peilin Zhao Junzhou Huang and Dinghao Wu. 2022. Local augmentation for graph neural networks. In ICML. PMLR 14054--14072."},{"key":"e_1_3_2_2_29_1","volume-title":"RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs. In Findings of ACL.","author":"Liu Xukai","year":"2023","unstructured":"Xukai Liu, Kai Zhang, Ye Liu, Enhong Chen, Zhenya Huang, Linan Yue, and Jiaxian Yan. 2023. RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs. In Findings of ACL."},{"key":"e_1_3_2_2_30_1","volume-title":"International Conference on Machine Learning. PMLR, 15524--15543","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and generalizable graph learning via stochastic attention mechanism. In International Conference on Machine Learning. PMLR, 15524--15543."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2068870"},{"key":"e_1_3_2_2_32_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3224067"},{"key":"e_1_3_2_2_34_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_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17623"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539366"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-020-0360-y"},{"key":"e_1_3_2_2_38_1","volume-title":"The information bottleneck method. arXiv preprint physics\/0004057","author":"Tishby Naftali","year":"2000","unstructured":"Naftali Tishby, Fernando C Pereira, and William Bialek. 2000. The information bottleneck method. arXiv preprint physics\/0004057 (2000)."},{"key":"e_1_3_2_2_39_1","volume-title":"Generalizing to unseen domains: A survey on domain generalization","author":"Wang Jindong","year":"2022","unstructured":"Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen,Wenjun Zeng, and Philip Yu. 2022. Generalizing to unseen domains: A survey on domain generalization. IEEE TKDE (2022)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00092"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494523"},{"key":"e_1_3_2_2_42_1","unstructured":"Ying-Xin Wu Xiang Wang An Zhang Xiangnan He and Tat seng Chua. 2022. Discovering Invariant Rationales for Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_2_43_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR."},{"key":"e_1_3_2_2_44_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems 32","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_2_46_1","first-page":"26603","article-title":"DARE: Disentanglement-Augmented Rationale Extraction","volume":"35","author":"Yue Linan","year":"2022","unstructured":"Linan Yue, Qi Liu, Yichao Du, Yanqing An, Li Wang, and Enhong Chen. 2022. DARE: Disentanglement-Augmented Rationale Extraction. In Advances in Neural Information Processing Systems, Vol. 35. 26603--26617.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_47_1","unstructured":"Linan Yue Qi Liu Yichao Du Li Wang and Weibo Gao. 2024. Boosting Selective Rationalization with Shortcuts Discovery. In ICLR."},{"key":"e_1_3_2_2_48_1","volume-title":"Interventional Rationalization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 11404--11418","author":"Yue Linan","year":"2023","unstructured":"Linan Yue, Qi Liu, Li Wang, Yanqing An, Yichao Du, and Zhenya Huang. 2023. Interventional Rationalization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 11404--11418."},{"key":"e_1_3_2_2_49_1","first-page":"6074","article-title":"Dynamic graph neural networks under spatio-temporal distribution shift","volume":"35","author":"Zhang Zeyang","year":"2022","unstructured":"Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, and Wenwu Zhu. 2022. Dynamic graph neural networks under spatio-temporal distribution shift. Advances in Neural Information Processing Systems 35 (2022), 6074--6089.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"}],"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\/3589334.3645332","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645332","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:34:55Z","timestamp":1755822895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":50,"alternative-id":["10.1145\/3589334.3645332","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645332","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"}}]}}