{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:08:20Z","timestamp":1784138900722,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Curtin University RTCM Trailblazer Project","award":["PRO-701177"],"award-info":[{"award-number":["PRO-701177"]}]},{"name":"Hong Kong Research Grants Council, General Research Fund","award":["P0047343"],"award-info":[{"award-number":["P0047343"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,20]]},"DOI":"10.1145\/3805712.3809673","type":"proceedings-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:06:26Z","timestamp":1784135186000},"page":"1846-1856","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3643-3353","authenticated-orcid":false,"given":"Xiangmeng","family":"Wang","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-9551","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"Curtin University, Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0363-1460","authenticated-orcid":false,"given":"Haiyang","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Macau, Taipa, Macao"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-7133","authenticated-orcid":false,"given":"Hao","family":"Miao","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"The Education University of Hong Kong, New Territories, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,19]]},"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."},{"key":"e_1_3_2_1_2_1","unstructured":"Peter W Battaglia Jessica B Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner et al. 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"e_1_3_2_1_4_1","volume-title":"Detecting shortcut learning for fair medical AI using shortcut testing. Nature communications","author":"Brown Alexander","year":"2023","unstructured":"Alexander Brown, Nenad Tomasev, Jan Freyberg, Yuan Liu, Alan Karthikesalingam, and Jessica Schrouff. 2023. Detecting shortcut learning for fair medical AI using shortcut testing. Nature communications, Vol. 14, 1 (2023), 4314."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614804"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3161453"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3267902"},{"key":"e_1_3_2_1_8_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)."},{"key":"e_1_3_2_1_9_1","first-page":"1","volume-title":"State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences. In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence","author":"Chou Jingyuan","year":"2024","unstructured":"Jingyuan Chou, Jiangzhuo Chen, and Madhav Marathe. 2024. State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences. In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 1-13."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1808"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583268"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00257-z"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/11564089_7"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583324"},{"key":"e_1_3_2_1_15_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","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, Vol. 30 (2017)."},{"key":"e_1_3_2_1_16_1","first-page":"14239","article-title":"Bernnet: Learning arbitrary graph spectral filters via bernstein approximation","volume":"34","author":"He Mingguo","year":"2021","unstructured":"Mingguo He, Zhewei Wei, Hongteng Xu, et al., 2021. Bernnet: Learning arbitrary graph spectral filters via bernstein approximation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 14239-14251.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120916"},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Database Systems for Advanced Applications. Springer, 164-179","author":"Huang Sirui","year":"2024","unstructured":"Sirui Huang, Qian Li, Xiangmeng Wang, Dianer Yu, Guandong Xu, and Qing Li. 2024. Counterfactual debasing for multi-behavior recommendations. In International Conference on Database Systems for Advanced Applications. Springer, 164-179."},{"key":"e_1_3_2_1_19_1","first-page":"5311","article-title":"Telecom fraud detection via hawkes-enhanced sequence model","volume":"35","author":"Jiang Yan","year":"2022","unstructured":"Yan Jiang, Guannan Liu, Junjie Wu, and Hao Lin. 2022. Telecom fraud detection via hawkes-enhanced sequence model. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 5 (2022), 5311-5324.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_1_20_1","volume-title":"Raw-gnn: Random walk aggregation based graph neural network. arXiv preprint arXiv:2206.13953","author":"Jin Di","year":"2022","unstructured":"Di Jin, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, and Weixiong Zhang. 2022. Raw-gnn: Random walk aggregation based graph neural network. arXiv preprint arXiv:2206.13953 (2022)."},{"key":"e_1_3_2_1_21_1","volume-title":"Universal graph convolutional networks. Advances in neural information processing systems","author":"Jin Di","year":"2021","unstructured":"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), 10654-10664."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441735"},{"key":"e_1_3_2_1_23_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)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736913"},{"key":"e_1_3_2_1_25_1","volume-title":"Be causal: De-biasing social network confounding in recommendation. ACM Transactions on Knowledge Discovery from Data","author":"Li Qian","year":"2023","unstructured":"Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. 2023. Be causal: De-biasing social network confounding in recommendation. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 1 (2023), 1-23."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3134200"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25573"},{"key":"e_1_3_2_1_28_1","unstructured":"Sitao Luan Chenqing Hua Qincheng Lu Liheng Ma Lirong Wu Xinyu Wang Minkai Xu Xiao-Wen Chang Doina Precup Rex Ying et al. 2024. The heterophilic graph learning handbook: Benchmarks models theoretical analysis applications and challenges. arXiv preprint arXiv:2407.09618 (2024)."},{"key":"e_1_3_2_1_29_1","volume-title":"Revisiting heterophily for graph neural networks. Advances in neural information processing systems","author":"Luan Sitao","year":"2022","unstructured":"Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2022. Revisiting heterophily for graph neural networks. Advances in neural information processing systems, Vol. 35 (2022), 1362-1375."},{"key":"e_1_3_2_1_30_1","volume-title":"Parameterized explainer for graph neural network. Advances in neural information processing systems","author":"Luo Dongsheng","year":"2020","unstructured":"Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized explainer for graph neural network. Advances in neural information processing systems, Vol. 33 (2020), 19620-19631."},{"key":"e_1_3_2_1_31_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)."},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3700-3708","author":"Ma Yunpu","year":"2021","unstructured":"Yunpu Ma and Volker Tresp. 2021. Causal inference under networked interference and intervention policy enhancement. In International Conference on Artificial Intelligence and Statistics. PMLR, 3700-3708."},{"key":"e_1_3_2_1_33_1","first-page":"20673","article-title":"Learning from failure: De-biasing classifier from biased classifier","volume":"33","author":"Nam Junhyun","year":"2020","unstructured":"Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin. 2020. Learning from failure: De-biasing classifier from biased classifier. Advances in Neural Information Processing Systems, Vol. 33 (2020), 20673-20684.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","unstructured":"Judea Pearl. 1998. Why there is no statistical test for confounding why many think there is and why they are almost right. (1998)."},{"key":"e_1_3_2_1_35_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_1_36_1","volume-title":"Cambridge, UK","author":"Judea Pearl","year":"2000","unstructured":"Judea Pearl et al., 2000. Models, reasoning and inference. Cambridge, UK: CambridgeUniversityPress, Vol. 19, 2 (2000)."},{"key":"e_1_3_2_1_37_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)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681312"},{"key":"e_1_3_2_1_39_1","volume-title":"When heterophily meets heterogeneous graphs: Latent graphs guided unsupervised representation learning","author":"Shen Zhixiang","year":"2025","unstructured":"Zhixiang Shen and Zhao Kang. 2025. When heterophily meets heterogeneous graphs: Latent graphs guided unsupervised representation learning. IEEE Transactions on Neural Networks and Learning Systems (2025)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645549"},{"key":"e_1_3_2_1_41_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)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3159802"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3629172"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3643670"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3354077"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512072"},{"key":"e_1_3_2_1_47_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00169"},{"key":"e_1_3_2_1_49_1","first-page":"4751","article-title":"Diverse message passing for attribute with heterophily","volume":"34","author":"Yang Liang","year":"2021","unstructured":"Liang Yang, Mengzhe Li, Liyang Liu, Chuan Wang, Xiaochun Cao, Yuanfang Guo, et al., 2021. Diverse message passing for attribute with heterophily. Advances in Neural Information Processing Systems, Vol. 34 (2021), 4751-4763.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_50_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems","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, Vol. 32 (2019)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3322403"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.01.089"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3543112"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v40i19.38656"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449845"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Tong Zhang and Bin Yu. 2005. Boosting with early stopping: Convergence and consistency. (2005).","DOI":"10.1214\/009053605000000255"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3324937"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2104"}],"event":{"name":"SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:21:43Z","timestamp":1784136103000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805712.3809673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"references-count":58,"alternative-id":["10.1145\/3805712.3809673","10.1145\/3805712"],"URL":"https:\/\/doi.org\/10.1145\/3805712.3809673","relation":{},"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"2026-07-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}