{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T03:48:07Z","timestamp":1775620087471,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225202, 62202029"],"award-info":[{"award-number":["62225202, 62202029"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAAI-Huawei MindSpore Open Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"DOI":"10.1145\/3616855.3635783","type":"proceedings-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T18:18:12Z","timestamp":1709576292000},"page":"920-929","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8539-3146","authenticated-orcid":false,"given":"Ziqi","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2393-3634","authenticated-orcid":false,"given":"Haoyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory &amp; School of Software, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0649-5799","authenticated-orcid":false,"given":"Tianyu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-0055","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory &amp; School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Saley Vishal Vivek, and M. Narasimha Murty","author":"Bandyopadhyay Sambaran","year":"2020","unstructured":"Sambaran Bandyopadhyay, Lokesh N, Saley Vishal Vivek, and M. Narasimha Murty. 2020. Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding. In WSDM. ACM, 25--33."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00982-2"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110030"},{"key":"e_1_3_2_1_4_1","volume-title":"Entity Embedding-Based Anomaly Detection for Heterogeneous Categorical Events","author":"Chen Ting","unstructured":"Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, and Kai Zhang. 2016. Entity Embedding-Based Anomaly Detection for Heterogeneous Categorical Events. In IJCAI. IJCAI\/AAAI Press, 1396--1403."},{"key":"e_1_3_2_1_5_1","volume-title":"Anomaly Detection from Log Data Sequences with Perturbations","author":"Chen Yixiang","unstructured":"Yixiang Chen, Linhao Ye, Yufeng Ye, Peng Zhang, and Qinfeng Tan. 2022. Anomaly Detection from Log Data Sequences with Perturbations. In DSC. IEEE, 183--190."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Min Du Feifei Li Guineng Zheng and Vivek Srikumar. 2017. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. In CCS. ACM 1285--1298.","DOI":"10.1145\/3133956.3134015"},{"key":"e_1_3_2_1_7_1","volume-title":"AANE: Anomaly Aware Network Embedding For Anomalous Link Detection","author":"Duan Dongsheng","year":"2020","unstructured":"Dongsheng Duan, Lingling Tong, Yangxi Li, Jie Lu, Lei Shi, and Cheng Zhang. 2020. AANE: Anomaly Aware Network Embedding For Anomalous Link Detection. In ICDM. IEEE, 1002--1007."},{"key":"e_1_3_2_1_8_1","volume-title":"Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks","author":"Fan Haoyi","year":"2020","unstructured":"Haoyi Fan, Fengbin Zhang, and Zuoyong Li. 2020. Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks. In ICASSP. IEEE, 5685--5689."},{"key":"e_1_3_2_1_9_1","volume-title":"Accessed","author":"Foundation Apache Software","year":"2005","unstructured":"Apache Software Foundation. 2005. SpamAssassin. [Online]. Available: https:\/\/spamassassin.apache.org\/old\/publiccorpus. Accessed: June 15, 2023."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Yuan Gao Xiang Wang Xiangnan He Zhenguang Liu Huamin Feng and Yongdong Zhang. 2023. Alleviating Structural Distribution Shift in Graph Anomaly Detection. In WSDM. ACM 357--365.","DOI":"10.1145\/3539597.3570377"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23083861"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"e_1_3_2_1_13_1","volume-title":"DASFAA (4) (Lecture Notes in Computer Science","author":"Guo Hongcheng","unstructured":"Hongcheng Guo, Yuhui Guo, Jian Yang, Jiaheng Liu, Zhoujun Li, Tieqiao Zheng, Liangfan Zheng, Weichao Hou, and Bo Zhang. 2023. LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction. In DASFAA (4) (Lecture Notes in Computer Science, Vol. 13946). Springer, 490--501."},{"key":"e_1_3_2_1_14_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034."},{"key":"e_1_3_2_1_15_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 (Poster). OpenReview.net."},{"key":"e_1_3_2_1_16_1","volume-title":"Aur\u00e9 lie C. Lozano, and Naoki Abe","author":"Kollias Georgios","year":"2022","unstructured":"Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Id\u00e9 , Aur\u00e9 lie C. Lozano, and Naoki Abe. 2022. Directed Graph Auto-Encoders. In AAAI. AAAI Press, 7211--7219."},{"key":"e_1_3_2_1_17_1","volume-title":"Semi-supervised Anomaly Detection on Attributed Graphs","author":"Kumagai Atsutoshi","unstructured":"Atsutoshi Kumagai, Tomoharu Iwata, and Yasuhiro Fujiwara. 2021. Semi-supervised Anomaly Detection on Attributed Graphs. In IJCNN. IEEE, 1--8."},{"key":"e_1_3_2_1_18_1","volume-title":"Mohammad Saiful Islam Mamun, and Ali A. Ghorbani","author":"Lashkari Arash Habibi","year":"2017","unstructured":"Arash Habibi Lashkari, Gerard Draper-Gil, Mohammad Saiful Islam Mamun, and Ali A. Ghorbani. 2017. Characterization of Tor Traffic using Time based Features. In ICISSP. SciTePress, 253--262."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03905-6"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.11.083"},{"key":"e_1_3_2_1_21_1","volume-title":"The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection","author":"Li Zhixun","unstructured":"Zhixun Li, Dingshuo Chen, Qiang Liu, and Shu Wu. 2022. The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection. In ICDM. IEEE, 1059--1064."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3068344"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03427-1"},{"key":"e_1_3_2_1_25_1","volume-title":"UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)","author":"Moustafa Nour","unstructured":"Nour Moustafa and Jill Slay. 2015. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In MilCIS. IEEE, 1--6."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.100.022314"},{"key":"e_1_3_2_1_27_1","volume-title":"Unified Graph Embedding-Based Anomalous Edge Detection","author":"Ouyang Linshu","unstructured":"Linshu Ouyang, Yongzheng Zhang, and Yipeng Wang. 2020. Unified Graph Embedding-Based Anomalous Edge Detection. In IJCNN. IEEE, 1--8."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543146.3543171"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Nitesh Suresh Sehwani. 2022. No Features Needed: Using BPE Sequence Embeddings for Web Log Anomaly Detection. In IWSPA@CODASPY. ACM 78--85.","DOI":"10.1145\/3510548.3519375"},{"key":"e_1_3_2_1_30_1","volume-title":"Wilsey","author":"Singh Rohit P.","year":"2022","unstructured":"Rohit P. Singh and Philip A. Wilsey. 2022. Polytopal Complex Construction and Use in Persistent Homology. In ICDM (Workshops). IEEE, 634--641."},{"key":"e_1_3_2_1_31_1","volume-title":"Yu","author":"Sun Qingyun","year":"2022","unstructured":"Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and Philip S. Yu. 2022a. Graph Structure Learning with Variational Information Bottleneck. In AAAI. AAAI Press, 4165--4174."},{"key":"e_1_3_2_1_32_1","volume-title":"SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism. In WWW. ACM \/ IW3C2","author":"Sun Qingyun","year":"2021","unstructured":"Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Philip S. Yu, and Lifang He. 2021. SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism. In WWW. ACM \/ IW3C2, 2081--2091."},{"key":"e_1_3_2_1_33_1","volume-title":"Yu","author":"Sun Qingyun","year":"2022","unstructured":"Qingyun Sun, Jianxin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, and Philip S. Yu. 2022b. Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing. In CIKM. ACM, 1848--1857."},{"key":"e_1_3_2_1_34_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008."},{"key":"e_1_3_2_1_35_1","unstructured":"Petar Velickovic William Fedus William L. Hamilton Pietro Li\u00f2 Yoshua Bengio and R. Devon Hjelm. 2019. Deep Graph Infomax. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05924-9"},{"key":"e_1_3_2_1_37_1","volume-title":"MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs. In ACSAC. ACM, 769--784.","author":"Wang Xiaolei","year":"2022","unstructured":"Xiaolei Wang, Lin Yang, Dongyang Li, Linru Ma, Yongzhong He, Junchao Xiao, Jiyuan Liu, and Yuexiang Yang. 2022. MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs. In ACSAC. ACM, 769--784."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5567991"},{"key":"e_1_3_2_1_39_1","volume-title":"HP-GMN: Graph Memory Networks for Heterophilous Graphs","author":"Xu Junjie","unstructured":"Junjie Xu, Enyan Dai, Xiang Zhang, and Suhang Wang. 2022. HP-GMN: Graph Memory Networks for Heterophilous Graphs. In ICDM. IEEE, 1263--1268."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0164-4"},{"key":"e_1_3_2_1_41_1","volume-title":"WPD-ResNeSt: Substation station level network anomaly traffic detection based on deep transfer learning. CSEE Journal of Power and Energy Systems","author":"Yang Ting","year":"2021","unstructured":"Ting Yang, Yucheng Hou, Yachuang Liu, Feng Zhai, and Rongze Niu. 2021. WPD-ResNeSt: Substation station level network anomaly traffic detection based on deep transfer learning. CSEE Journal of Power and Energy Systems (2021)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109860"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-020-10404-7"},{"key":"e_1_3_2_1_44_1","volume-title":"CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences. In KDD. ACM, 4541--4550.","author":"Zhang Shengming","year":"2022","unstructured":"Shengming Zhang, Yanchi Liu, Xuchao Zhang, Wei Cheng, Haifeng Chen, and Hui Xiong. 2022a. CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences. In KDD. ACM, 4541--4550."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Li Zheng Zhenpeng Li Jian Li Zhao Li and Jun Gao. 2019. AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN. In IJCAI. ijcai.org 4419--4425.","DOI":"10.24963\/ijcai.2019\/614"},{"key":"e_1_3_2_1_46_1","volume-title":"Dillon","author":"Zipperle Michael","year":"2023","unstructured":"Michael Zipperle, Florian Gottwalt, Elizabeth Chang, and Tharam S. Dillon. 2023. Provenance-based Intrusion Detection Systems: A Survey. ACM Comput. Surv. , Vol. 55, 7 (2023), 135:1--135:36. io"}],"event":{"name":"WSDM '24: The 17th ACM International Conference on Web Search and Data Mining","location":"Merida Mexico","acronym":"WSDM '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 17th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616855.3635783","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3616855.3635783","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:56:12Z","timestamp":1755824172000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616855.3635783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":46,"alternative-id":["10.1145\/3616855.3635783","10.1145\/3616855"],"URL":"https:\/\/doi.org\/10.1145\/3616855.3635783","relation":{},"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"2024-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}