{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:02:18Z","timestamp":1789646538566,"version":"4.0.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"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":["World Wide Web"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1007\/s11280-026-01429-4","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:09:10Z","timestamp":1789643350000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Temporal graph anomaly detection based on multi-view semi-supervised learning"],"prefix":"10.1007","volume":"29","author":[{"given":"Yifan","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangzheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"issue":"7","key":"1429_CR1","doi-asserted-by":"publisher","first-page":"2254","DOI":"10.1109\/JSAC.2021.3078497","volume":"39","author":"Z Li","year":"2021","unstructured":"Li, Z., Rios, A.L.G., Trajkovic, L.: Machine learning for detecting anomalies and intrusions in communication networks. IEEE J. Sel. Areas Commun. 39(7), 2254\u20132264 (2021)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"1429_CR2","doi-asserted-by":"crossref","unstructured":"Miao, K., Shi, X., Zhang, W.A.: Attack signal estimation for intrusion detection in industrial control system. Comput. Secur. 96(101926), (2020)","DOI":"10.1016\/j.cose.2020.101926"},{"key":"1429_CR3","doi-asserted-by":"publisher","first-page":"47699","DOI":"10.1109\/ACCESS.2022.3170467","volume":"10","author":"B Dumitrescu","year":"2022","unstructured":"Dumitrescu, B., Baltoiu, A., Budulan, S.: Anomaly detection in graphs of bank transactions for anti money laundering applications. IEEE Access 10, 47699\u201347714 (2022)","journal-title":"IEEE Access"},{"issue":"4","key":"1429_CR4","first-page":"4731","volume":"34","author":"X Li","year":"2020","unstructured":"Li, X., Liu, S., Li, Z., Han, X., Shi, C., Hooi, B., Huang, H., Cheng, X.: Flowscope: Spotting money laundering based on graphs. Proceed. AAAI Conf. Artif. Intell. 34(4), 4731\u20134738 (2020)","journal-title":"Proceed. AAAI Conf. Artif. Intell."},{"key":"1429_CR5","doi-asserted-by":"crossref","unstructured":"Pourhabibi, T., Ong, K.L., Kam, B.H., Boo, Y.L.: Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decis. Support Syst. 133(113303), (2020)","DOI":"10.1016\/j.dss.2020.113303"},{"key":"1429_CR6","doi-asserted-by":"crossref","unstructured":"Peng, Z., Luo, M., Li, J., Liu, H., Zheng, Q.: Anomalous: A joint modeling approach for anomaly detection on attributed networks. In: Proceeding of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3513\u20133519 (2018)","DOI":"10.24963\/ijcai.2018\/488"},{"key":"1429_CR7","doi-asserted-by":"crossref","unstructured":"Li, J., Dani, H., Hu, X., Liu, H.: Radar: Residual analysis for anomaly detection in attributed networks. In: Proceeding of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2152\u20132158 (2017)","DOI":"10.24963\/ijcai.2017\/299"},{"key":"1429_CR8","doi-asserted-by":"crossref","unstructured":"Chen, T., Tsourakakis, C.E.: Antibenford subgraphs: Unsupervised anomaly detection in financial networks. In: Proceeding of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (2022)","DOI":"10.1145\/3534678.3539100"},{"key":"1429_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhao, L., Zhu, X., Ranka, S., Thai, M., Washio, T.: Unsupervised deep subgraph anomaly detection. In: Wu, X. (ed.) Proceeding of the 2022 IEEE International Conference on Data Mining (ICDM). IEEE International Conference on Data Mining, pp. 753\u2013762. (2022)","DOI":"10.1109\/ICDM54844.2022.00086"},{"key":"1429_CR10","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: Proceeding of the 30th ACM International Conference on Information & Knowledge Management, pp. 3122\u20133126 (2021)","DOI":"10.1145\/3459637.3482057"},{"key":"1429_CR11","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. (2021)"},{"key":"1429_CR12","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. Proceedings of the AAAI Conference on Artificial Intelligence 37(6), 7459\u20137467 (2023)","DOI":"10.1609\/aaai.v37i6.25907"},{"key":"1429_CR13","unstructured":"Wu, F., Zhang, T., Souza, A.H. Jr., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. In: Proceeding of the 36th International Conference on Machine Learning (ICML), vol. 97 (2019)"},{"key":"1429_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., Chen, H.: Decoupling representation learning and classification for gnn-based anomaly detection. In: Proceeding of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 1239\u20131248 (2021)","DOI":"10.1145\/3404835.3462944"},{"key":"1429_CR15","unstructured":"Tang, J., Li, J., Gao, Z., Li, J.: Rethinking graph neural networks for anomaly detection. In: Proceeding of the 39th International Conference on Machine Learning (ICML) (2022)"},{"key":"1429_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wu, J., Yang, J., Beheshti, A., Xue, S., Zhou, C., Sheng, Q.Z.: Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance. In: Bailey, J., Miettinen, P., Koh, Y., Tao, D., Wu, X. (eds.) Proceeding of the 21st IEEE International Conference on Data Mining (IEEE ICDM). IEEE International Conference on Data Mining, pp. 867\u2013876 (2021)","DOI":"10.1109\/ICDM51629.2021.00098"},{"key":"1429_CR17","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceeding of the international conference on learning representations, (2017)"},{"key":"1429_CR18","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: Proceeding of the international conference on learning representations, (2018)"},{"key":"1429_CR19","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceeding of the 31st Annual Conference on Neural Information Processing Systems (NIPS), vol. 30 (2017)"},{"issue":"7024","key":"1429_CR20","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1038\/nature03248","volume":"433","author":"CM Song","year":"2005","unstructured":"Song, C.M., Havlin, S., Makse, H.A.: Self-similarity of complex networks. Nature 433(7024), 392\u2013395 (2005)","journal-title":"Nature"},{"key":"1429_CR21","doi-asserted-by":"crossref","unstructured":"Luo, X., Wu, J., Beheshti, A., Yang, J., Zhang, X., Wang, Y., Xue, S.: Comga: Community-aware attributed graph anomaly detection. In: Proceeding of the 15th ACM international conference on Web Search and Data Mining (WSDM), pp. 657\u2013665 (2022)","DOI":"10.1145\/3488560.3498389"},{"key":"1429_CR22","doi-asserted-by":"crossref","unstructured":"Ding, K., Li, J., Bhanushali, R., Liu, H.: Deep Anomaly Detection on Attributed Networks, pp. 594\u2013602 (2019)","DOI":"10.1137\/1.9781611975673.67"},{"key":"1429_CR23","doi-asserted-by":"crossref","unstructured":"Fan, H., Zhang, F., Li, Z.: Anomalydae: Dual autoencoder for anomaly detection on attributed networks. In: Proceeding of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5685\u20135689 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053387"},{"issue":"6","key":"1429_CR24","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TNNLS.2021.3068344","volume":"33","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C., Karypis, G.: Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE Trans. Neural Netw. Learn. Syst. 33(6), 2378\u20132392 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1429_CR25","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: Daume, H., Singh, A. (eds.) Proceeding of the International Conference on Machine Learning (ICML), vol. 119 (2020)"},{"key":"1429_CR26","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: Proceeding of the international conference on learning representations, (2019)"},{"key":"1429_CR27","doi-asserted-by":"publisher","first-page":"106151","DOI":"10.1016\/j.neunet.2024.106151","volume":"172","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Yang, P., Fan, X., Yan, X., Wu, Z., Pan, S., Chen, L., Zang, Y., Wang, C., Yu, R.: Contig: Continuous representation learning on temporal interaction graphs. Neural Netw. 172, 106151 (2024)","journal-title":"Neural Netw."},{"key":"1429_CR28","doi-asserted-by":"crossref","unstructured":"Tian, S., Dong, J., Li, J., Zhao, W., Xu, X., Wang, B., Song, B., Meng, C., Zhang, T., Chen, L.: Sad: semi-supervised anomaly detection on dynamic graphs. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 2306\u20132314 (2023)","DOI":"10.24963\/ijcai.2023\/256"},{"key":"1429_CR29","doi-asserted-by":"crossref","unstructured":"Chen, J., Fu, S., Ma, Z., Feng, M., Wirjanto, T.S., Peng, Q.: Semi-supervised anomaly detection with extremely limited labels in dynamic graphs. In: International Conference on Database Systems for Advanced Applications, pp. 373\u2013382. Springer (2025)","DOI":"10.1007\/978-981-95-3830-0_24"},{"key":"1429_CR30","unstructured":"Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., Achan, K.: Inductive representation learning on temporal graphs. In: International Conference on Learning Representations (2020)."},{"key":"1429_CR31","doi-asserted-by":"crossref","unstructured":"Qiao, H., Pang, G.: Truncated affinity maximization: One-class homophily modeling for graph anomaly detection. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)","DOI":"10.52202\/075280-2154"},{"key":"1429_CR32","doi-asserted-by":"publisher","first-page":"12073","DOI":"10.1007\/s00521-021-05924-9","volume":"33","author":"X Wang","year":"2020","unstructured":"Wang, X., Jin, B., Du, Y., Cui, P., Yang, Y.: One-class graph neural networks for anomaly detection in attributed networks. Neural Comput. Appl. 33, 12073\u201312085 (2020)","journal-title":"Neural Comput. Appl."},{"key":"1429_CR33","doi-asserted-by":"crossref","unstructured":"Wang, D., Qi, Y., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., Yu, Q., Zhou, J., Yang, S.: A semi-supervised graph attentive network for financial fraud detection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 598\u2013607 (2019)","DOI":"10.1109\/ICDM.2019.00070"},{"key":"1429_CR34","doi-asserted-by":"crossref","unstructured":"Ding, K., Zhou, Q., Tong, H., Liu, H.: Few-shot network anomaly detection via cross-network meta-learning. In: Proceedings of the Web Conference, pp. 2021 (2021)","DOI":"10.1145\/3442381.3449922"},{"key":"1429_CR35","unstructured":"Ruff, L., Vandermeulen, R.A., G\u00f6rnitz, N., Binder, A., M\u00fcller, E., M\u00fcller, K.-R., Kloft, M.: Deep semi-supervised anomaly detection. In: International Conference on Learning Representations (2020)"},{"key":"1429_CR36","doi-asserted-by":"crossref","unstructured":"Kumagai, A., Iwata, T., Fujiwara, Y.: Semi-supervised anomaly detection on attributed graphs. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. (2021)","DOI":"10.1109\/IJCNN52387.2021.9533507"},{"issue":"12","key":"1429_CR37","doi-asserted-by":"publisher","first-page":"12081","DOI":"10.1109\/TKDE.2021.3124061","volume":"35","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Pan, S., Wang, Y.G., Xiong, F., Wang, L., Chen, Q., Lee, V.C.: Anomaly detection in dynamic graphs via transformer. IEEE Trans. Knowl. Data Eng. 35(12), 12081\u201312094 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1429_CR38","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, S., Shin, K.: Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD \u201924, pp. 1506\u20131517. Association for Computing Machinery, New York, NY, USA (2024)","DOI":"10.1145\/3637528.3671845"},{"issue":"20","key":"1429_CR39","first-page":"22001","volume":"39","author":"X Yang","year":"2025","unstructured":"Yang, X., Zhao, X., Shen, Z.: A generalizable anomaly detection method in dynamic graphs. Proceed. AAAI Conf. Artif. Intell. 39(20), 22001\u201322009 (2025)","journal-title":"Proceed. AAAI Conf. Artif. Intell."}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-026-01429-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-026-01429-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-026-01429-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:09:24Z","timestamp":1789643364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-026-01429-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"references-count":39,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["1429"],"URL":"https:\/\/doi.org\/10.1007\/s11280-026-01429-4","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]},"assertion":[{"value":"18 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that there are no competing interests of any kind, whether financial or non-financial. The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"45"}}