{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:12:36Z","timestamp":1784178756690,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Application Foundation Frontier Project of Wuhan Science and Technology Bureau","award":["2020010601012288"],"award-info":[{"award-number":["2020010601012288"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["19ZDA113"],"award-info":[{"award-number":["19ZDA113"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Nature Science Foundation of China","award":["U22A2035, U1803262, U1736206"],"award-info":[{"award-number":["U22A2035, U1803262, U1736206"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3681312","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"11032-11040","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Heterophilic Graph Invariant Learning for Out-of-Distribution of Fraud Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3756-3427","authenticated-orcid":false,"given":"Lingfei","family":"Ren","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5872-3872","authenticated-orcid":false,"given":"Ruimin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3846-9157","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9827-6149","authenticated-orcid":false,"given":"Yilin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3349-8664","authenticated-orcid":false,"given":"Dengshi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jianghan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0297-4995","authenticated-orcid":false,"given":"Junhang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Shihezi University, Shihezi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8535-071X","authenticated-orcid":false,"given":"Yilong","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5370-7718","authenticated-orcid":false,"given":"Jinzhang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3142-9294","authenticated-orcid":false,"given":"Zijun","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3540261.3540524"},{"key":"e_1_3_2_1_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_1_3_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/270"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 37th Int'l Conf. on machine learning. 1448--1458","author":"Chang Shiyu","year":"2020","unstructured":"Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola. 2020. Invariant rationalization. In Proceedings of the 37th Int'l Conf. on machine learning. 1448--1458."},{"key":"e_1_3_2_1_5_1","unstructured":"Jingyu Chen Runlin Lei and Zhewei Wei. 2024. PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters. In The Twelfth Int'l Conf. on Learning Representations. 1--13."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 37th Int'l Conf. on machine learning. 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 Proceedings of the 37th Int'l Conf. on machine learning. 1597--1607."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3600270.3601878"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575646"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411903"},{"key":"e_1_3_2_1_10_1","first-page":"322","article-title":"Generalizing graph neural networks on out-of-distribution graphs","volume":"40","author":"Fan Shaohua","year":"2023","unstructured":"Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, and Bai Wang. 2023. Generalizing graph neural networks on out-of-distribution graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, 1 (2023), 322--337.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"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":"10.1145\/3539597.3570377"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 37th Int'l Conf. on machine learning. 4116--4126","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In Proceedings of the 37th Int'l Conf. on machine learning. 4116--4126."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3613780"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 4th Int'l Conf. on Learning Representations. 1--12","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 4th Int'l Conf. on Learning Representations. 1--12."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the 38th Int'l Conf. on Machine Learning. 5815--5826","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 Proceedings of the 38th Int'l Conf. on Machine Learning. 5815--5826."},{"key":"e_1_3_2_1_17_1","volume-title":"Out-of-distribution generalization on graphs: A survey. arXiv preprint arXiv:2202.07987","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022. Out-of-distribution generalization on graphs: A survey. arXiv preprint arXiv:2202.07987 (2022)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3600270.3601129"},{"key":"e_1_3_2_1_19_1","volume-title":"A survey of graph meets large language model: Progress and future directions. arXiv preprint arXiv:2311.12399","author":"Li Yuhan","year":"2023","unstructured":"Yuhan Li, Zhixun Li, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, and Jeffrey Xu Yu. 2023. A survey of graph meets large language model: Progress and future directions. arXiv preprint arXiv:2311.12399 (2023)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00036"},{"key":"e_1_3_2_1_21_1","volume-title":"Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs. arXiv preprint arXiv:2402.11153","author":"Liu Shuhan","year":"2024","unstructured":"Shuhan Liu and Kaize Ding. 2024. Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs. arXiv preprint arXiv:2402.11153 (2024)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599355"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449989"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25573"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401253"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110888"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512195"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539366"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the 38th Int'l Conf. on Machine Learning","author":"Tang Jianheng","year":"2021","unstructured":"Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li. 2021. Rethinking Graph Neural Networks for Anomaly Detection. Proceedings of the 38th Int'l Conf. on Machine Learning (2021), 12761--12771."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 9th Int'l Conf. on Learning Representations. 1--13","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Velivckovi\u0107, and Michal Valko. 2021. Large-Scale Representation Learning on Graphs via Bootstrapping. In Proceedings of the 9th Int'l Conf. on Learning Representations. 1--13."},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the 6th Int'l Conf. on Learning Representations. 1--12","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2018. Deep Graph Infomax. In Proceedings of the 6th Int'l Conf. on Learning Representations. 1--12."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119153"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583373"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615067"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20984-0_44"},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the 9th Int'l Conf. on Learning Representations. 1--13","author":"Wu Qitian","year":"2021","unstructured":"Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf. 2021. Handling Distribution Shifts on Graphs: An Invariance Perspective. In Proceedings of the 9th Int'l Conf. on Learning Representations. 1--13."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3250523"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28773"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.5555\/3600270.3601212"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496212"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00098"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.5555\/3540261.3540268"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3271771"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the 35th Advances in Neural Information Processing Systems","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. Proceedings of the 35th Advances in Neural Information Processing Systems (2021), 27965--27977."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681312","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3681312","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:43Z","timestamp":1750295863000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":45,"alternative-id":["10.1145\/3664647.3681312","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3681312","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}