{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T15:55:27Z","timestamp":1767801327526,"version":"3.49.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059611","type":"print"},{"value":"9783032059628","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05962-8_9","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T19:38:06Z","timestamp":1759520286000},"page":"141-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HAGAN: Homophily-Aware Generative Adversarial Network for\u00a0Graph Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Wenkai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Fan","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Meihong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Bandyopadhyay, S., N, L., Vivek, S.V., Murty, M.N.: Outlier resistant unsupervised deep architectures for attributed network embedding. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 25\u201333 (2020)","DOI":"10.1145\/3336191.3371788"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"9_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110030","volume":"258","author":"E Caville","year":"2022","unstructured":"Caville, E., Lo, W.W., Layeghy, S., Portmann, M.: Anomal-e: a self-supervised network intrusion detection system based on graph neural networks. Knowl.-Based Syst. 258, 110030 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"9_CR4","unstructured":"Chen, B., et al.: Graph contrastive learning for anomaly detection (2021). https:\/\/api.semanticscholar.org\/CorpusID:251799764"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Liu, B., Wang, M., Dai, P., Lv, J., Bo, L.: Generative adversarial attributed network anomaly detection. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1989\u20131992 (2020)","DOI":"10.1145\/3340531.3412070"},{"issue":"12","key":"9_CR6","doi-asserted-by":"publisher","first-page":"12444","DOI":"10.1109\/TKDE.2023.3272396","volume":"35","author":"D Cheng","year":"2023","unstructured":"Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., Jiang, C.: Anti-money laundering by group-aware deep graph learning. IEEE Trans. Knowl. Data Eng. 35(12), 12444\u201312457 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Ding, K., Li, J., Agarwal, N., Liu, H.: Inductive anomaly detection on attributed networks. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 1288\u20131294 (2021)","DOI":"10.24963\/ijcai.2020\/179"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Ding, K., Li, J., Bhanushali, R., Liu, H.: Deep anomaly detection on attributed networks. In: Proceedings of the 2019 SIAM international conference on data mining. pp. 594\u2013602. SIAM (2019)","DOI":"10.1137\/1.9781611975673.67"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., Yu, P.S.: Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 315\u2013324 (2020)","DOI":"10.1145\/3340531.3411903"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Duan, J., Wang, S., Zhang, P., Zhu, E., Hu, J., Jin, H., Liu, Y., Dong, Z.: Graph anomaly detection via multi-scale contrastive learning networks with augmented view. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 7459\u20137467 (2023)","DOI":"10.1609\/aaai.v37i6.25907"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Gao, Y., Wang, X., He, X., Liu, Z., Feng, H., Zhang, Y.: Alleviating structural distribution shift in graph anomaly detection. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 357\u2013365 (2023)","DOI":"10.1145\/3539597.3570377"},{"key":"9_CR12","unstructured":"Gasteiger, J., Wei\u00dfenberger, S., G\u00fcnnemann, S.: Diffusion improves graph learning. Advances in neural information processing systems 32 (2019)"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Gong, Z., Wang, G., Sun, Y., Liu, Q., Ning, Y., Xiong, H., Peng, J.: Beyond homophily: Robust graph anomaly detection via neural sparsification. In: IJCAI, pp. 2104\u20132113 (2023)","DOI":"10.24963\/ijcai.2023\/234"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. Commun. ACM 63, 139\u2013144 (2014). https:\/\/api.semanticscholar.org\/CorpusID:1033682","DOI":"10.1145\/3422622"},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220\u2013239 (2017)","journal-title":"Expert Syst. Appl."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Huang, M., et al.: Auc-oriented graph neural network for fraud detection. In: Proceedings of the ACM Web Conference 2022, pp. 1311\u20131321 (2022)","DOI":"10.1145\/3485447.3512178"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Jin, M., Liu, Y., Zheng, Y., Chi, L., Li, Y.F., Pan, S.: Anemone: graph anomaly detection with multi-scale contrastive learning. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3122\u20133126 (2021)","DOI":"10.1145\/3459637.3482057"},{"issue":"6","key":"9_CR18","first-page":"3069","volume":"14","author":"W Khan","year":"2022","unstructured":"Khan, W., Haroon, M.: An efficient framework for anomaly detection in attributed social networks. Int. J. Inf. Technol. 14(6), 3069\u20133076 (2022)","journal-title":"Int. J. Inf. Technol."},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"111820","DOI":"10.1109\/ACCESS.2022.3211306","volume":"10","author":"H Kim","year":"2022","unstructured":"Kim, H., Lee, B.S., Shin, W.Y., Lim, S.: Graph anomaly detection with graph neural networks: current status and challenges. IEEE Access 10, 111820\u2013111829 (2022)","journal-title":"IEEE Access"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Kotha, P., Janardhan\u00a0Babu, V., Ankam, S.: Generative adversarial networks: a comprehensive review. In: International Conference on Computer & Communication Technologies, pp. 105\u2013114. Springer (2023)","DOI":"10.1007\/978-981-99-9704-6_9"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269\u20131278 (2019)","DOI":"10.1145\/3292500.3330895"},{"key":"9_CR22","unstructured":"Li, D.: Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758 (2018)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., Dani, H., Hu, X., Liu, H.: Radar: Residual analysis for anomaly detection in attributed networks. In: IJCAI, vol.\u00a017, pp. 2152\u20132158 (2017)","DOI":"10.24963\/ijcai.2017\/299"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Lin, M., Wen, K., Zhu, X., Zhao, H., Sun, X.: Graph autoencoder with preserving node attribute similarity. Entropy 25 (2023), https:\/\/api.semanticscholar.org\/CorpusID:257790352","DOI":"10.3390\/e25040567"},{"issue":"1","key":"9_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","volume":"6","author":"FT Liu","year":"2012","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discovery Data (TKDD) 6(1), 1\u201339 (2012)","journal-title":"ACM Trans. Knowl. Discovery Data (TKDD)"},{"issue":"12","key":"9_CR26","doi-asserted-by":"publisher","first-page":"12081","DOI":"10.1109\/TKDE.2021.3124061","volume":"35","author":"Y Liu","year":"2021","unstructured":"Liu, Y., et al.: Anomaly detection in dynamic graphs via transformer. IEEE Trans. Knowl. Data Eng. 35(12), 12081\u201312094 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Ngo, P.C., Winarto, A.A., Kou, C.K.L., Park, S., Akram, F., Lee, H.K.: Fence gan: towards better anomaly detection. In: 2019 IEEE 31St International Conference on tools with artificial intelligence (ICTAI), pp. 141\u2013148. IEEE (2019)","DOI":"10.1109\/ICTAI.2019.00028"},{"key":"9_CR28","volume-title":"The pagerank citation ranking: Bringing order to the web","author":"L Page","year":"1999","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Tech. rep, Stanford infolab (1999)"},{"key":"9_CR29","unstructured":"Pawar, D.: Advancements and applications of generative adversarial networks: a comprehensive review. Int. J. Res. Appl. Sci. Eng. Technol. (2024). https:\/\/api.semanticscholar.org\/CorpusID:269903498"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Peng, Z., Luo, M., Li, J., Liu, H., Zheng, Q., et\u00a0al.: Anomalous: a joint modeling approach for anomaly detection on attributed networks. In: IJCAI, vol.\u00a018, pp. 3513\u20133519 (2018)","DOI":"10.24963\/ijcai.2018\/488"},{"key":"9_CR31","unstructured":"Qiao, H., Pang, G.: Truncated affinity maximization: One-class homophily modeling for graph anomaly detection. Advances in Neural Information Processing Systems 36 (2024)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging, pp. 146\u2013157. Springer (2017)","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Shi, F., Cao, Y., Shang, Y., Zhou, Y., Zhou, C., Wu, J.: H2-fdetector: a gnn-based fraud detector with homophilic and heterophilic connections. In: Proceedings of the ACM Web Conference 2022, pp. 1486\u20131494 (2022)","DOI":"10.1145\/3485447.3512195"},{"key":"9_CR34","unstructured":"Tang, J., Li, J., Gao, Z., Li, J.: Rethinking graph neural networks for anomaly detection. In: International Conference on Machine Learning, pp. 21076\u201321089. PMLR (2022)"},{"key":"9_CR35","unstructured":"Vaswani, A., et al.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"issue":"1","key":"9_CR36","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TKDE.2024.3488375","volume":"37","author":"R Wang","year":"2025","unstructured":"Wang, R., Xi, L., Zhang, F., Fan, H., Yu, X., Liu, L., Yu, S., Leung, V.C.M.: Context correlation discrepancy analysis for graph anomaly detection. IEEE Trans. Knowl. Data Eng. 37(1), 174\u2013187 (2025). https:\/\/doi.org\/10.1109\/TKDE.2024.3488375","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"18","key":"9_CR37","doi-asserted-by":"publisher","first-page":"12073","DOI":"10.1007\/s00521-021-05924-9","volume":"33","author":"X Wang","year":"2021","unstructured":"Wang, X., Jin, B., Du, Y., Cui, P., Tan, Y., Yang, Y.: One-class graph neural networks for anomaly detection in attributed networks. Neural Comput. Appl. 33(18), 12073\u201312085 (2021). https:\/\/doi.org\/10.1007\/s00521-021-05924-9","journal-title":"Neural Comput. Appl."},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Xu, Z., Huang, X., Zhao, Y., Dong, Y., Li, J.: Contrastive attributed network anomaly detection with data augmentation. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 444\u2013457. Springer (2022)","DOI":"10.1007\/978-3-031-05936-0_35"},{"key":"9_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, S., Chen, S.: Reconstruction enhanced multi-view contrastive learning for anomaly detection on attributed networks. arXiv preprint arXiv:2205.04816 (2022)","DOI":"10.24963\/ijcai.2022\/330"},{"key":"9_CR40","unstructured":"Zhang, R., et al.: Generation is better than modification: combating high class homophily variance in graph anomaly detection. arXiv preprint arXiv:2403.10339 (2024)"},{"issue":"12","key":"9_CR41","doi-asserted-by":"publisher","first-page":"12220","DOI":"10.1109\/TKDE.2021.3119326","volume":"35","author":"Y Zheng","year":"2021","unstructured":"Zheng, Y., Jin, M., Liu, Y., Chi, L., Phan, K.T., Chen, Y.P.P.: Generative and contrastive self-supervised learning for graph anomaly detection. IEEE Trans. Knowl. Data Eng. 35(12), 12220\u201312233 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05962-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T13:06:47Z","timestamp":1767791207000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05962-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"ISBN":["9783032059611","9783032059628"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05962-8_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"4 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}