{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:46:30Z","timestamp":1777873590933,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3304701"],"award-info":[{"award-number":["2023YFC3304701"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62132017, U2436209"],"award-info":[{"award-number":["62132017, U2436209"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Natural Science Foundation","award":["ZQ2022JQ32"],"award-info":[{"award-number":["ZQ2022JQ32"]}]},{"name":"Beijing Natural Science Foundation","award":["L247027"],"award-info":[{"award-number":["L247027"]}]},{"DOI":"10.13039\/501100006374","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Funds of Renmin University of China"},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["2022QNRC001"],"award-info":[{"award-number":["2022QNRC001"]}]},{"name":"Big Data and Responsible Artificial Intelligence for National Governance, Renmin University of China"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736989","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T20:52:41Z","timestamp":1754254361000},"page":"3122-3133","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph Evidential Learning for Anomaly Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5802-5759","authenticated-orcid":false,"given":"Chunyu","family":"Wei","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1370-9337","authenticated-orcid":false,"given":"Wenji","family":"Hu","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5464-2609","authenticated-orcid":false,"given":"Xingjia","family":"Hao","sequence":"additional","affiliation":[{"name":"Guangxi University, Nanning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0059-6580","authenticated-orcid":false,"given":"Yunhai","family":"Wang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2239-4472","authenticated-orcid":false,"given":"Yueguo","family":"Chen","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6953-1948","authenticated-orcid":false,"given":"Bing","family":"Bai","sequence":"additional","affiliation":[{"name":"Microsoft MAI, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9459-9461","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Cornell University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2438"},{"key":"e_1_3_2_2_2_1","first-page":"3436","volume-title":"Continual Evidential Deep Learning for Out-of-Distribution Detection. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023 - Workshops","author":"Aguilar Eduardo","year":"2023","unstructured":"Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, and Joost van de Weijer. 2023. Continual Evidential Deep Learning for Out-of-Distribution Detection. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023 - Workshops, Paris, France, October 2-6, 2023. IEEE, 3436-3446."},{"key":"e_1_3_2_2_3_1","volume-title":"Deep evidential regression. Advances in neural information processing systems","author":"Amini Alexander","year":"2020","unstructured":"Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus. 2020. Deep evidential regression. Advances in neural information processing systems, Vol. 33 (2020), 14927-14937."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330112"},{"key":"e_1_3_2_2_5_1","volume-title":"Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining","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 '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, James Caverlee, Xia (Ben) Hu, Mounia Lalmas, and Wei Wang(Eds.). ACM, 25-33."},{"key":"e_1_3_2_2_6_1","unstructured":"Yuanchen Bei Sheng Zhou Jinke Shi Yao Ma Haishuai Wang and Jiajun Bu. 2024. Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection. arXiv preprint arXiv:2404.16366(2024)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127339"},{"key":"e_1_3_2_2_8_1","volume-title":"International conference on machine learning. PMLR, 1613-1622","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural network. In International conference on machine learning. PMLR, 1613-1622."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_3_2_2_11_1","volume-title":"Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts. Advances in neural information processing systems","author":"Charpentier Bertrand","year":"2020","unstructured":"Bertrand Charpentier, Daniel Z\u00fcgner, and Stephan G\u00fcnnemann. 2020. Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts. Advances in neural information processing systems, Vol. 33 (2020), 1356-1367."},{"key":"e_1_3_2_2_12_1","volume-title":"AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Chen Tianyi","year":"2022","unstructured":"Tianyi Chen and Charalampos E. Tsourakakis. 2022. AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, Aidong Zhang and Huzefa Rangwala(Eds.). ACM, 2762-2770."},{"key":"e_1_3_2_2_13_1","first-page":"1989","volume-title":"Generative Adversarial Attributed Network Anomaly Detection. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","author":"Chen Zhenxing","year":"2020","unstructured":"Zhenxing Chen, Bo Liu, Meiqing Wang, Peng Dai, Jun Lv, and Liefeng Bo. 2020. Generative Adversarial Attributed Network Anomaly Detection. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudr\u00e9-Mauroux(Eds.). ACM, 1989-1992."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11213500"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-44792-4_4"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.104132"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Persi Diaconis and Donald Ylvisaker. 1979. Conjugate priors for exponential families. The Annals of statistics(1979) 269-281.","DOI":"10.1214\/aos\/1176344611"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.3182\/20130902-3-CN-3020.00044"},{"key":"e_1_3_2_2_21_1","first-page":"5685","volume-title":"Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks. In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020","author":"Fan Haoyi","year":"2020","unstructured":"Haoyi Fan, Fengbin Zhang, and Zuoyong Li. 2020. Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks. In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain, May 4-8, 2020. IEEE, 5685-5689."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpa.2021.100079"},{"key":"e_1_3_2_2_23_1","unstructured":"Yarin Gal and Zoubin Ghahramani. 2015. Dropout as a Bayesian approximation. arXiv preprint arXiv:1506.02157(2015)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23052687"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116429"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2882404"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548082"},{"key":"e_1_3_2_2_29_1","volume-title":"In-network PCA and anomaly detection. Advances in neural information processing systems","author":"Huang Ling","year":"2006","unstructured":"Ling Huang, XuanLong Nguyen, Minos Garofalakis, Michael Jordan, Anthony Joseph, and Nina Taft. 2006. In-network PCA and anomaly detection. Advances in neural information processing systems, Vol. 19 (2006)."},{"key":"e_1_3_2_2_30_1","volume-title":"Subjective Logic - A Formalism for Reasoning Under Uncertainty","author":"J\u00f8sang Audun","unstructured":"Audun J\u00f8sang. 2016. Subjective Logic - A Formalism for Reasoning Under Uncertainty. Springer."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3211306"},{"key":"e_1_3_2_2_32_1","first-page":"95931","article-title":"Rethinking reconstruction-based graph-level anomaly detection: Limitations and a simple remedy","volume":"37","author":"Kim Sunwoo","year":"2024","unstructured":"Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, and Kijung Shin. 2024. Rethinking reconstruction-based graph-level anomaly detection: Limitations and a simple remedy. Advances in Neural Information Processing Systems, Vol. 37 (2024), 95931-95962.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_33_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016a. Variational Graph Auto-Encoders. CoRR, Vol. abs\/1611.07308 (2016)."},{"key":"e_1_3_2_2_34_1","unstructured":"Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308(2016)."},{"key":"e_1_3_2_2_35_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 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533507"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_2_38_1","volume-title":"Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems","author":"Lakshminarayanan Balaji","year":"2017","unstructured":"Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/299"},{"key":"e_1_3_2_2_40_1","volume-title":"Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder","author":"Li Longyuan","year":"2020","unstructured":"Longyuan Li, Junchi Yan, Haiyang Wang, and Yaohui Jin. 2020. Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder. IEEE transactions on neural networks and learning systems, Vol. 32, 3 (2020), 1177-1191."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1229285.1229292"},{"key":"e_1_3_2_2_42_1","volume-title":"Yu","author":"Liu Kay","year":"2022","unstructured":"Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, and Philip S. Yu. 2022b. BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh(Eds.)."},{"key":"e_1_3_2_2_43_1","volume-title":"MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging. arXiv preprint arXiv:2405.02918(2024).","author":"Liu Yuanye","year":"2024","unstructured":"Yuanye Liu, Zheyao Gao, Nannan Shi, Fuping Wu, Yuxin Shi, Qingchao Chen, and Xiahai Zhuang. 2024. MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging. arXiv preprint arXiv:2405.02918(2024)."},{"key":"e_1_3_2_2_44_1","unstructured":"Zhiyuan Liu Chunjie Cao and Jingzhang Sun. 2022a. Mul-gad: a semi-supervised graph anomaly detection framework via aggregating multi-view information. arXiv preprint arXiv:2212.05478(2022)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace7080115"},{"key":"e_1_3_2_2_47_1","unstructured":"Rhiannon Michelmore Marta Kwiatkowska and Yarin Gal. 2018. Evaluating uncertainty quantification in end-to-end autonomous driving control. arXiv preprint arXiv:1811.06817(2018)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2013.6547453"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Anvardh Nanduri and Lance Sherry. 2016. Anomaly detection in aircraft data using Recurrent Neural Networks (RNN). In 2016 Integrated Communications Navigation and Surveillance (ICNS). Ieee 5C2-1.","DOI":"10.1109\/ICNSURV.2016.7486356"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110796"},{"key":"e_1_3_2_2_51_1","volume-title":"Deep learning for anomaly detection: A review. ACM computing surveys (CSUR)","author":"Pang Guansong","year":"2021","unstructured":"Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. 2021. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR), Vol. 54, 2 (2021), 1-38."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Giorgio Parisi and Ramamurti Shankar. 1988. Statistical field theory. (1988).","DOI":"10.1063\/1.2811677"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/488"},{"key":"e_1_3_2_2_54_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 8th International Conference on Learning Representations, ICLR 2020","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635767"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"e_1_3_2_2_57_1","first-page":"647","volume-title":"Statistical Selection of Congruent Subspaces for Mining Attributed Graphs. In 2013 IEEE 13th International Conference on Data Mining","author":"S\u00e1nchez Patricia Iglesias","year":"2013","unstructured":"Patricia Iglesias S\u00e1nchez, Emmanuel M\u00fcller, Fabian Laforet, Fabian Keller, and Klemens B\u00f6hm. 2013. Statistical Selection of Congruent Subspaces for Mining Attributed Graphs. In 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013, Hui Xiong, George Karypis, Bhavani Thuraisingham, Diane J. Cook, and Xindong Wu(Eds.). IEEE Computer Society, 647-656."},{"key":"e_1_3_2_2_58_1","volume-title":"Pauline Lienhua Chou, and Qingmai Wang","author":"Savage David","year":"2016","unstructured":"David Savage, Xiuzhen Zhang, Xinghuo Yu, Pauline Lienhua Chou, and Qingmai Wang. 2016. Anomaly detection in online social networks. CoRR, Vol. abs\/1608.00301 (2016)."},{"key":"e_1_3_2_2_59_1","volume-title":"Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems","author":"Sensoy Murat","year":"2018","unstructured":"Murat Sensoy, Lance Kaplan, and Melih Kandemir. 2018. Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3089511"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281280"},{"key":"e_1_3_2_2_62_1","volume-title":"PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II(Lecture Notes in Computer Science","volume":"457","author":"Xu Zhiming","year":"2022","unstructured":"Zhiming Xu, Xiao Huang, Yue Zhao, Yushun Dong, and Jundong Li. 2022. Contrastive Attributed Network Anomaly Detection with Data Augmentation. In Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II(Lecture Notes in Computer Science, Vol. 13281), Jo a, o Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, and Fei Teng(Eds.). Springer, 444-457."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118196"},{"key":"e_1_3_2_2_64_1","first-page":"2691","volume-title":"Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (Big Data)","author":"Yuan Xu","year":"2021","unstructured":"Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, and Feng Xia. 2021. Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, December 15-18, 2021, Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama M. Fayyad, Xingquan Zhu, Xiaohua Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, and Carlos Ordonez(Eds.). IEEE, 2691-2700."},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3272731"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103916"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2021.0069"},{"key":"e_1_3_2_2_68_1","first-page":"1873","volume-title":"Error-Bounded Graph Anomaly Loss for GNNs. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","author":"Zhao Tong","year":"2020","unstructured":"Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, and Meng Jiang. 2020. Error-Bounded Graph Anomaly Loss for GNNs. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudr\u00e9-Mauroux(Eds.). ACM, 1873-1882."},{"key":"e_1_3_2_2_69_1","volume-title":"Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering. CoRR","author":"Zheng Lecheng","year":"2024","unstructured":"Lecheng Zheng, John R. Birge, Yifang Zhang, and Jingrui He. 2024. Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering. CoRR, Vol. abs\/2409.09770 (2024)."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/614"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011286"}],"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\/3711896.3736989","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:05:20Z","timestamp":1777572320000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":71,"alternative-id":["10.1145\/3711896.3736989","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736989","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"}}]}}