{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:05:46Z","timestamp":1780675546126,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","funder":[{"name":"Shanghai Pujiang Program","award":["24PJA004"],"award-info":[{"award-number":["24PJA004"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["25D111213"],"award-info":[{"award-number":["25D111213"]}]},{"name":"the National Natural Science Foundation of China","award":["92570118, 62376088, 62202095"],"award-info":[{"award-number":["92570118, 62376088, 62202095"]}]},{"name":"Hebei Natural Science Foundation","award":["F2024202047"],"award-info":[{"award-number":["F2024202047"]}]},{"name":"Hebei Yanzhao Golden Platform Talent Gathering Programme Core Talent Project (Education Platform)","award":["HJZD202509"],"award-info":[{"award-number":["HJZD202509"]}]},{"name":"Open Project Program of the State Key Laboratory of CAD&CG, Zhejiang University","award":["A2411"],"award-info":[{"award-number":["A2411"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792429","type":"proceedings-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:38:33Z","timestamp":1777293513000},"page":"1068-1079","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizable Graph-level Anomaly Detection via Prompted Anomaly Expansion and Normality Extraction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6009-780X","authenticated-orcid":false,"given":"Ge","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Intelligent Science, Donghua University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8007-8643","authenticated-orcid":false,"given":"Jiapei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information and Intelligent Science, Donghua University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0310-0598","authenticated-orcid":false,"given":"Guohao","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Intelligent Science, Donghua University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1527-9270","authenticated-orcid":false,"given":"Xiu","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information and Intelligent Science, Donghua University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4094-4295","authenticated-orcid":false,"given":"Zhenyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6645-0597","authenticated-orcid":false,"given":"Xixun","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6291-4359","authenticated-orcid":false,"given":"Liang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00098"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.132"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Cai Jinyu","year":"2025","unstructured":"Jinyu Cai, Yunhe Zhang, and Jicong Fan. 2025. Self-Discriminative Modeling for Anomalous Graph Detection. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00659"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Chen Nan","year":"2024","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 Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-49730-7_9"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Dong Xiangyu","year":"2024","unstructured":"Xiangyu Dong, Xingyi Zhang, and Sibo Wang. 2024. Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_8_1","first-page":"52464","article-title":"Universal Prompt Tuning for Graph Neural Networks","volume":"36","author":"Fang Taoran","year":"2023","unstructured":"Taoran Fang, Yunchao Zhang, YANG YANG, Chunping Wang, and Lei Chen. 2023. Universal Prompt Tuning for Graph Neural Networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36. 52464-52489.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Fu Xingbo","year":"2025","unstructured":"Xingbo Fu, Yinhan He, and Jundong Li. 2025. Edge Prompt Tuning for Graph Neural Networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_10_1","volume-title":"Unveiling Fine-grained Deceptive Patterns in Multi-modal Fake News: An Explainable Neuro-Symbolic Framework with LVLMs","author":"He Dongxiao","year":"2025","unstructured":"Dongxiao He, Yiqi Dong, Xiaobao Wang, Meng Ge, Carl Yang, Di Jin, and Witold Pedrycz. 2025. Unveiling Fine-grained Deceptive Patterns in Multi-modal Fake News: An Explainable Neuro-Symbolic Framework with LVLMs. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2025\/335"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972825.71"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0195-3"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671845"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714879"},{"key":"e_1_3_2_1_16_1","volume-title":"Hallucinations: A Survey of Taxonomy, Methods, and Directions. ArXiv Preprint ArXiv:2509.18970","author":"Lin Xixun","year":"2025","unstructured":"Xixun Lin, Yucheng Ning, Jingwen Zhang, Yan Dong, Yilong Liu, Yongxuan Wu, Xiaohua Qi, Nan Sun, Yanmin Shang, Kun Wang, et al., 2025b. LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions. ArXiv Preprint ArXiv:2509.18970 (2025)."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems.","author":"Lin Yiqing","year":"2024","unstructured":"Yiqing Lin, Jianheng Tang, Chenyi Zi, H. Vicky Zhao, Yuan Yao, and Jia Li. 2024. UniGAD: Unifying Multi-level Graph Anomaly Detection. In Proceedings of the Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_18_1","first-page":"8975","article-title":"Towards self-interpretable graph-level anomaly detection","volume":"36","author":"Liu Yixin","year":"2023","unstructured":"Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, and Shirui Pan. 2023a. Towards self-interpretable graph-level anomaly detection. Advances in Neural Information Processing Systems, Vol. 36 (2023), 8975-8987.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems.","author":"Liu Yixin","year":"2024","unstructured":"Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, and Shirui Pan. 2024. ARC: A Generalist Graph Anomaly Detector with In-Context Learning. In Proceedings of the Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498473"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the International Conference on Machine Learning. 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 Proceedings of the International Conference on Machine Learning. 15524-15543."},{"key":"e_1_3_2_1_25_1","volume-title":"Revisiting graph neural networks: All we have is low-pass filters. ArXiv Preprint ArXiv:1905.09550","author":"Nt Hoang","year":"2019","unstructured":"Hoang Nt and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. ArXiv Preprint ArXiv:1905.09550 (2019)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13174-010-0003-x"},{"key":"e_1_3_2_1_27_1","volume-title":"Deep graph anomaly detection: A survey and new perspectives","author":"Qiao Hezhe","year":"2025","unstructured":"Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, and Guansong Pang. 2025. Deep graph anomaly detection: A survey and new perspectives. IEEE Transactions on Knowledge and Data Engineering (2025)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/305"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v40i18.38601"},{"key":"e_1_3_2_1_30_1","article-title":"Weisfeiler-lehman graph kernels","volume":"12","author":"Shervashidze Nino","year":"2011","unstructured":"Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt. 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, Vol. 12, 9 (2011).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33369"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. 4165-4174","author":"Sun Qingyun","unstructured":"Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and Philip S. Yu. 2022. Graph Structure Learning with Variational Information Bottleneck. In Proceedings of the AAAI Conference on Artificial Intelligence. 4165-4174."},{"key":"e_1_3_2_1_33_1","volume-title":"Graph prompt learning: A comprehensive survey and beyond. ArXiv Preprint ArXiv:2311.16534","author":"Sun Xiangguo","year":"2023","unstructured":"Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li. 2023. Graph prompt learning: A comprehensive survey and beyond. ArXiv Preprint ArXiv:2311.16534 (2023)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3554053"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems.","author":"Wang Xiaobao","year":"2025","unstructured":"Xiaobao Wang, Ruoxiao Sun, Yujun Zhang, Bingdao Feng, Dongxiao He, Luzhi Wang, and Di Jin. 2025. Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification. In Proceedings of the Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2999032"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512156"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3746027.3754821"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3695863","article-title":"State of the art and potentialities of graph-level learning","volume":"57","author":"Yang Zhenyu","year":"2024","unstructured":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, et al., 2024. State of the art and potentialities of graph-level learning. ACM Computing Surveys, Vol. 57, 2 (2024), 1-40.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736983"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645423"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00098"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2025.3584090"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3647221"},{"key":"e_1_3_2_1_47_1","volume-title":"Aggarwal","author":"Zhang Ge","year":"2022","unstructured":"Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng, Leman Akoglu, and Charu C. Aggarwal. 2022. Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2021.0069"},{"key":"e_1_3_2_1_49_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems Datasets and Benchmarks Track.","author":"Zi Chenyi","year":"2024","unstructured":"Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, and Jia Li. 2024. ProG: A Graph Prompt Learning Benchmark. In Proceedings of the Conference on Neural Information Processing Systems Datasets and Benchmarks Track."}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774904.3792429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:45:49Z","timestamp":1780674349000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":49,"alternative-id":["10.1145\/3774904.3792429","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792429","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}