{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:48:43Z","timestamp":1776365323633,"version":"3.51.2"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272285"],"award-info":[{"award-number":["62272285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Basic Research Program of Shanxi Province","award":["202403021221193"],"award-info":[{"award-number":["202403021221193"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Graph anomaly detection (GAD) aims to identify nodes or edges that deviate from normal patterns. However, the presence of heterophilic edges in graphs leads to feature over-smoothing issues. To overcome this limitation, this paper proposes the multi-view heterogeneity resistant network (MV-GHRN) model, which progressively purifies heterophilic edges through multi-view collaboration. First, to address the noise sensitivity of single predictions, the method computes post-aggregation (PA) scores for both the original graph and its perturbed versions and performs weighted fusion, leveraging the consistency of multiple prediction perspectives to enhance the reliability of heterophilic edge identification. Second, a cosine similarity view is introduced as a complementary structural perspective, with both views independently completing heterophilic edge pruning to clean the graph structure from both topological and feature dimensions. Finally, a cross-view self-distillation mechanism is designed, using the fused predictions from the two purified views as teacher signals to guide the optimization of each view in reverse, correcting feature biases caused by heterophilic edges. Experiments on benchmark datasets such as YelpChi and Amazon demonstrate that the framework significantly outperforms existing methods. For instance, on the YelpChi dataset, MV-GHRN surpasses the best baseline by 16.8% and 5.2% in F1-Macro and AUC, respectively, validating the effectiveness of the progressive multi-view purification mechanism.<\/jats:p>","DOI":"10.3390\/info16110985","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:33:21Z","timestamp":1763141601000},"page":"985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph Anomaly Detection Algorithm Based on Multi-View Heterogeneity Resistant Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Yangrui","family":"Fan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-8446","authenticated-orcid":false,"given":"Caixia","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9930-9088","authenticated-orcid":false,"given":"Hui","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2962-6334","authenticated-orcid":false,"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[{"name":"James att School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"ref_1","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-supervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_2","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_3","unstructured":"Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., and Koutra, D. (2020, January 6\u201312). Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Proceedings of the Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, J., Rossi, R.A., Rao, A., Mai, T., Lipka, N., Ahmed, N.K., and Koutra, D. (2021, January 2\u20139). Graph Neural Networks with Heterophily. Proceedings of the AAAI Conference on Artificial Intelligence, Menlo Park, CA, USA.","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., and Yu, P.S. (2020, January 19\u201323). Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. Proceedings of the ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland.","DOI":"10.1145\/3340531.3411903"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101299","DOI":"10.1016\/j.measen.2024.101299","article-title":"A Modified CNN-IDS Model for Enhancing the Efficacy of Intrusion Detection System","volume":"35","author":"Abed","year":"2024","journal-title":"Meas. Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gao, Y., Wang, X., He, X., Liu, Z., Feng, H., and Zhang, Y. (May, January 30). Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. Proceedings of the ACM Web Conference 2023, Austin, TX, USA.","DOI":"10.1145\/3543507.3583268"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103708","DOI":"10.1016\/j.artint.2022.103708","article-title":"Multi-View Graph Convolutional Networks with Attention Mechanism","volume":"307","author":"Yao","year":"2022","journal-title":"Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1504\/IJHM.2024.140573","article-title":"Multi-objective optimisation of micromixer design using genetic algorithms and multi-criteria decision-making algorithms","volume":"7","author":"Cunegatto","year":"2024","journal-title":"Int. J. Hydromechatron."},{"key":"ref_10","unstructured":"Xu, C., Tao, D., and Xu, C. (2013). A Survey on Multi-view Learning. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Xiong, Y., Zhang, J., Zhang, Y., Zhang, T., and Zhu, Y. (2020). Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning. arXiv.","DOI":"10.1109\/ICDM50108.2020.00031"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1504\/IJHM.2023.132303","article-title":"Single-pixel image reconstruction based on block compressive sensing and convolutional neural network","volume":"6","author":"Lau","year":"2023","journal-title":"Int. J. Hydromechatron."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cui, L., Seo, H., Tabar, M., Ma, F., Wang, S., and Dong, Y. (2020, January 6\u201310). DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3403092"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, C., Sun, L., Ao, X., Yang, J., Feng, H., and He, Q. (2021, January 14\u201318). Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore.","DOI":"10.1145\/3447548.3467142"},{"key":"ref_15","unstructured":"Zhao, T., Liu, Y., Neves, L., Woodford, O., Jiang, M., and Shah, N. (2021, January 2\u20139). Data Augmentation for Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, Menlo Park, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, M., Liu, Y., Ao, X., Li, K., Chi, J., Feng, J., Yang, H., and He, Q. (2022, January 25\u201329). AUC-oriented Graph Neural Network for Fraud Detection. Proceedings of the World Wide Web Conference, Lyon, France.","DOI":"10.1145\/3485447.3512178"},{"key":"ref_17","unstructured":"Chien, E., Peng, J., Li, P., and Milenkovic, O. (2021, January 3\u20137). Adaptive Universal Generalized PageRank Graph Neural Network. Proceedings of the International Conference on Learning Representations, Virtual Event."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, M., Qiao, Y., and Lee, B. (2025). Multi-View Intrusion Detection Framework Using Deep Learning and Knowledge Graphs. Information, 16.","DOI":"10.3390\/info16050377"},{"key":"ref_19","first-page":"119008","article-title":"Detect Anomalies on Multi-View Attributed Networks","volume":"638","author":"Chen","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Duan, J., Wang, S., Zhang, P., Zhu, E., Hu, J., Jin, H., Liu, Y., and Dong, Z. (2023, January 7\u201314). Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Washington, DC, USA.","DOI":"10.1609\/aaai.v37i6.25907"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, T., Ni, B., Yu, W., Cao, J., Mao, Z., and Liu, C. (2021, January 1\u20135). Action Sequence Augmentation for Early Graph-based Anomaly Detection. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Queensland, Australia.","DOI":"10.1145\/3459637.3482313"},{"key":"ref_22","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.i., and Jegelka, S. (2018, January 10\u201315). Representation Learning on Graphs with Jumping Knowledge Networks. Proceedings of the 35th International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021, January 19\u201323). Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection. Proceedings of the 30th International World Wide Web Conference, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3449989"},{"key":"ref_24","unstructured":"Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Ver Steeg, G., and Galstyan, A. (2019, January 9\u201315). MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Proceedings of the 36th International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_25","unstructured":"Bo, D., Wang, X., Shi, C., and Shen, H. (2021, January 2\u20139). Beyond Low-frequency Information in Graph Convolutional Networks. Proceedings of the AAAI Conference on Artificial Intelligence, Menlo Park, CA, USA."},{"key":"ref_26","unstructured":"Tang, J., Li, J., Gao, Z., and Li, J. (2022, January 17\u201323). Rethinking Graph Neural Networks for Anomaly Detection. Proceedings of the 39th International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, F., Wang, N., Wu, H., Zhang, X., Yu, K., Yuan, Y., and Tang, X. (2024, January 20\u201327). Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum. Proceedings of the 38th AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i8.28773"},{"key":"ref_28","unstructured":"Zhuo, W., Liu, Z., Hooi, B., Wang, B., He, G., Cai, K., and Li, X. (2024, January 7\u201311). Partitioning Message Passing for Graph Fraud Detection. Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_29","unstructured":"Tang, J., Hua, F., Gao, Z., Zhao, P., and Li, J. (2023, January 10\u201316). GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2023)\u2014Datasets and Benchmarks Track, Red Hook, NY, USA."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:36:18Z","timestamp":1763141778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["info16110985"],"URL":"https:\/\/doi.org\/10.3390\/info16110985","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]}}}