{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:47:12Z","timestamp":1777873632636,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","funder":[{"name":"Hong Kong RGC GRF grant","award":["No. 14217322"],"award-info":[{"award-number":["No. 14217322"]}]},{"name":"Tencent WeChat Rhino-Bird Focused Research Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736877","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T20:54:17Z","timestamp":1754254457000},"page":"1565-1576","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3403-7714","authenticated-orcid":false,"given":"Xiping","family":"Li","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6312-8160","authenticated-orcid":false,"given":"Xiangyu","family":"Dong","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5203-5916","authenticated-orcid":false,"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8921-5531","authenticated-orcid":false,"given":"Kun","family":"Xie","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7738-3461","authenticated-orcid":false,"given":"Yuanhao","family":"Feng","sequence":"additional","affiliation":[{"name":"Tencent, WeChat Pay, ShenZhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9426-8741","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Tencent, WeChat Pay, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9349-6513","authenticated-orcid":false,"given":"Guilin","family":"Li","sequence":"additional","affiliation":[{"name":"Tencent, WeChat Pay, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1301-5371","authenticated-orcid":false,"given":"Wuxiong","family":"Zeng","sequence":"additional","affiliation":[{"name":"Tencent, WeChat Pay, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-9298","authenticated-orcid":false,"given":"Xiujun","family":"Shu","sequence":"additional","affiliation":[{"name":"Tencent, WeChat Pay, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1892-6971","authenticated-orcid":false,"given":"Sibo","family":"Wang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Source Code. https:\/\/github.com\/HsipingLi\/ChiGAD."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1049\/ip-com:20010659"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3259144"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2044016"},{"key":"e_1_3_2_2_5_1","first-page":"1945","article-title":"Can abnormality be detected by graph neural networks?","author":"Chai Ziwei","year":"2022","unstructured":"Ziwei Chai, Siqi You, Yang Yang, Shiliang Pu, Jiarong Xu, Haoyang Cai, and Weihao Jiang. 2022. Can abnormality be detected by graph neural networks?. In IJCAI. 1945-1951.","journal-title":"IJCAI."},{"key":"e_1_3_2_2_6_1","unstructured":"Nan Chen Zemin Liu Bryan Hooi Bingsheng He Rizal Fathony Jun Hu and Jia Chen. 2024. Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision. In ICLR."},{"key":"e_1_3_2_2_7_1","first-page":"281","article-title":"Multi-relational Link Prediction in Heterogeneous Information Networks","author":"Davis Darcy A.","year":"2011","unstructured":"Darcy A. Davis, Ryan Lichtenwalter, and Nitesh V. Chawla. 2011. Multi-relational Link Prediction in Heterogeneous Information Networks. In ASONAM. 281-288.","journal-title":"ASONAM."},{"key":"e_1_3_2_2_8_1","first-page":"849","article-title":"Leonhard euler's integral: A historical profile of the gamma function: In memoriam: Milton abramowitz","volume":"66","author":"Davis Philip J","year":"1959","unstructured":"Philip J Davis. 1959. Leonhard euler's integral: A historical profile of the gamma function: In memoriam: Milton abramowitz. The American Mathematical Monthly 66, 10 (1959), 849-869.","journal-title":"The American Mathematical Monthly"},{"key":"e_1_3_2_2_9_1","unstructured":"Xiangyu Dong Xingyi Zhang Lei Chen Mingxuan Yuan and Sibo Wang. 2025. SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels. In ICLR."},{"key":"e_1_3_2_2_10_1","first-page":"1225","article-title":"SmoothGNN","author":"Dong Xiangyu","year":"2025","unstructured":"Xiangyu Dong, Xingyi Zhang, Yanni Sun, Lei Chen, Mingxuan Yuan, and Sibo Wang. 2025. SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection. In WWW. 1225-1236.","journal-title":"Smoothing-aware GNN for Unsupervised Node Anomaly Detection. In WWW."},{"key":"e_1_3_2_2_11_1","unstructured":"Xiangyu Dong Xingyi Zhang and Sibo Wang. 2024. Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection. In ICLR."},{"key":"e_1_3_2_2_12_1","first-page":"181","article-title":"Link Prediction and Recommendation across Heterogeneous Social Networks","author":"Dong Yuxiao","year":"2012","unstructured":"Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V. Chawla, Jinghai Rao, and Huanhuan Cao. 2012. Link Prediction and Recommendation across Heterogeneous Social Networks. In ICDM. 181-190.","journal-title":"ICDM."},{"key":"e_1_3_2_2_13_1","first-page":"2331","article-title":"MAGNN","author":"Fu Xinyu","year":"2020","unstructured":"Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In WWW. 2331-2341.","journal-title":"In WWW."},{"key":"e_1_3_2_2_14_1","first-page":"1528","article-title":"Addressing Heterophily in Graph Anomaly Detection","author":"Gao Yuan","year":"2023","unstructured":"Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. 2023. Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. In WWW. 1528-1538.","journal-title":"A Perspective of Graph Spectrum. In WWW."},{"key":"e_1_3_2_2_15_1","first-page":"1024","article-title":"Inductive Representation Learning on Large Graphs","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024-1034.","journal-title":"NeurIPS."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"e_1_3_2_2_17_1","first-page":"685","article-title":"Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials","author":"He Mingguo","year":"2024","unstructured":"Mingguo He, Zhewei Wei, Shikun Feng, Zhengjie Huang, Weibin Li, Yu Sun, and Dianhai Yu. 2024. Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials. In WWW. 685-696.","journal-title":"WWW."},{"key":"e_1_3_2_2_18_1","first-page":"14239","article-title":"Bern-Net: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation","author":"He Mingguo","year":"2021","unstructured":"Mingguo He, Zhewei Wei, Zengfeng Huang, and Hongteng Xu. 2021. Bern-Net: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. In NeurIPS. 14239-14251.","journal-title":"NeurIPS."},{"key":"e_1_3_2_2_19_1","volume-title":"Introduction to numerical analysis","author":"Hildebrand Francis Begnaud","unstructured":"Francis Begnaud Hildebrand. 1987. Introduction to numerical analysis. Courier Corporation."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_2_21_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_22_1","first-page":"179","article-title":"Inferring anchor links across multiple heterogeneous social networks","author":"Kong Xiangnan","year":"2013","unstructured":"Xiangnan Kong, Jiawei Zhang, and Philip S. Yu. 2013. Inferring anchor links across multiple heterogeneous social networks. In CIKM. 179-188.","journal-title":"CIKM."},{"key":"e_1_3_2_2_23_1","volume-title":"A link prediction method for heterogeneous networks based on BP neural network. Physica A: Statistical Mechanics and its Applications 495","author":"Ge Bing-Feng","year":"2018","unstructured":"Ji-chao Li, Dan-ling Zhao, Bing-Feng Ge, Ke-Wei Yang, and Ying-Wu Chen. 2018. A link prediction method for heterogeneous networks based on BP neural network. Physica A: Statistical Mechanics and its Applications 495 (2018), 1-17."},{"key":"e_1_3_2_2_24_1","first-page":"3734","article-title":"Category-based and Popularity-guided Video Game Recommendation","author":"Li Xiping","year":"2024","unstructured":"Xiping Li, Jianghong Ma, Kangzhe Liu, Shanshan Feng, Haijun Zhang, and Yutong Wang. 2024. Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework. In WWW. 3734-3744.","journal-title":"A Balance-oriented Framework. In WWW."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-021-00723-z"},{"key":"e_1_3_2_2_26_1","first-page":"3168","article-title":"Pick and Choose","author":"Liu Yang","year":"2021","unstructured":"Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. 2021. Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection. In WWW. 3168-3177.","journal-title":"A GNN-based Imbalanced Learning Approach for Fraud Detection. In WWW."},{"key":"e_1_3_2_2_27_1","first-page":"2077","article-title":"Heterogeneous Graph Neural Networks for Malicious Account Detection","author":"Liu Ziqi","year":"2018","unstructured":"Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018. Heterogeneous Graph Neural Networks for Malicious Account Detection. In CIKM. 2077-2085.","journal-title":"CIKM."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/837601"},{"key":"e_1_3_2_2_29_1","first-page":"5425","article-title":"Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs","author":"Monti Federico","year":"2017","unstructured":"Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodol\u00e0, Jan Svoboda, and Michael M. Bronstein. 2017. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. In CVPR. 5425-5434.","journal-title":"CVPR."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2833443"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3137597.3137600"},{"key":"e_1_3_2_2_33_1","unstructured":"Jianheng Tang Fengrui Hua Ziqi Gao Peilin Zhao and Jia Li. 2023. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. In NeurIPS."},{"key":"e_1_3_2_2_34_1","first-page":"21076","article-title":"Rethinking Graph Neural Networks for Anomaly Detection","author":"Tang Jianheng","year":"2022","unstructured":"Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li. 2022. Rethinking Graph Neural Networks for Anomaly Detection. In ICML. 21076-21089.","journal-title":"ICML."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Lloyd N Trefethen. 2019. Approximation theory and approximation practice extended edition. SIAM.","DOI":"10.1137\/1.9781611975949"},{"key":"e_1_3_2_2_36_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_37_1","volume-title":"Neil Zhenqiang Gong, and Hao Fu","author":"Wang Binghui","year":"2017","unstructured":"Binghui Wang, Neil Zhenqiang Gong, and Hao Fu. 2017. GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs. In ICDM. 465-474."},{"key":"e_1_3_2_2_38_1","first-page":"2022","article-title":"Heterogeneous Graph Attention Network","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous Graph Attention Network. In WWW. 2022-2032.","journal-title":"WWW."},{"key":"e_1_3_2_2_39_1","first-page":"406","article-title":"Label Information Enhanced Fraud Detection against Low Homophily in Graphs","author":"Wang Yuchen","year":"2023","unstructured":"Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, and Junzhou Luo. 2023. Label Information Enhanced Fraud Detection against Low Homophily in Graphs. In WWW. 406-416.","journal-title":"WWW."},{"key":"e_1_3_2_2_40_1","unstructured":"Bingbing Xu Huawei Shen Qi Cao Yunqi Qiu and Xueqi Cheng. 2019. Graph Wavelet Neural Network. In ICLR."},{"key":"e_1_3_2_2_41_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_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556259"},{"key":"e_1_3_2_2_43_1","first-page":"793","article-title":"Heterogeneous Graph Neural Network","author":"Zhang Chuxu","year":"2019","unstructured":"Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla. 2019. Heterogeneous Graph Neural Network. In KDD. 793-803.","journal-title":"KDD."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783268"},{"key":"e_1_3_2_2_45_1","first-page":"2742","article-title":"Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator","author":"Zhang Yusong","year":"2025","unstructured":"Yusong Zhang, Kun Xie, Xingyi Zhang, Xiangyu Dong, and Sibo Wang. 2025. Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator. In WWW. 2742-2753.","journal-title":"WWW."},{"key":"e_1_3_2_2_46_1","first-page":"1534","article-title":"Relation Structure-Aware Heterogeneous Graph Neural Network","author":"Zhu Shichao","year":"2019","unstructured":"Shichao Zhu, Chuan Zhou, Shirui Pan, Xingquan Zhu, and Bin Wang. 2019. Relation Structure-Aware Heterogeneous Graph Neural Network. In ICDM. 1534-1539.","journal-title":"ICDM."}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736877","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:06:52Z","timestamp":1777572412000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":46,"alternative-id":["10.1145\/3711896.3736877","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736877","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}