Graph-based fraud detection plays a critical role in identifying anomalous accounts and preventing financial losses in real-world systems, where graphs often contain millions of nodes but only a limited number of blacklist labels are available. Existing graph neural network approaches typically rely on full-graph message passing and sufficient supervision, which leads to weak neighborhood aggregation under scarce labels and poor scalability on large-scale graphs. In this work, we propose a scalable fraud detection framework that integrates prototype-guided graph abstraction with dualfrequency filtering to effectively learn from limited blacklists. Specifically, we construct class prototypes from the scarce labeled anomalies to capture compact class-level patterns and use them to guide subgraph abstraction and edge pruning, reducing the computational burden of graph message passing. On the resulting abstracted subgraphs, we apply dual-frequency graph filters to jointly model low-frequency homogeneous signals and high-frequency heterogeneous interactions. To further enhance robustness under incomplete supervision, we leverage high-confidence pseudo-labels to refine the graph abstraction and perform a second round of efficient dual-frequency filtering for prediction refinement. Extensive experiments on four benchmark datasets, including a large-scale graph, demonstrate that the proposed method consistently outperforms state-of-the-art approaches.
- Open Access
- Article
Learning with Blacklists on Graphs via Prototype-Guided Dual-Frequency Filtering
Author Information
Received: 23 May 2026 | Revised: 04 Aug 2026 | Accepted: 04 Aug 2026 | Published: 06 Aug 2026
Abstract
References
- 1.
Chen, J.; Chen, Q.; Jiang, F.; et al. SCN GNN: A GNN-Based Fraud Detection Algorithm Combining Strong Node and Graph Topology Information. Expert Syst. Appl. 2024, 237, 121643.
- 2.
Liu, Z.; Yu, H.; Luo, X. Mitigating Label Noise in Graph Learning with Information Bottleneck-Guided Aggregation and Adversarial Consistency. Pattern Recognit. 2026, 180, 114271.
- 3.
Liu, Z.; Yu, H.; Luo, X. Federated Graph Anomaly Detection via Disentangled Representation Learning. In Proceedings of the ACM on Web Conference 2025, Sydney, NSW, Australia, 28 April–2 May 2025; pp. 1216–1224.
- 4.
McAuley, J.J.; Leskovec, J. From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise Through Online Reviews. In Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13–17 May 2013; pp. 897–908.
- 5.
Liu, Z.; Yu, H.; Luo, X. A Noise-Resistant Model for Graph-Based Fraud Detection. Inf. Process. Manag. 2025, 62, 104198.
- 6.
Weber, M.; Domeniconi, G.; Chen, J.; et al. Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. arXiv 2019, arXiv:1908.02591.
- 7.
Tang, J.; Li, J.; Gao, Z.; et al. Rethinking Graph Neural Networks for Anomaly Detection. In Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, USA, 17–23 July 2022; pp. 21076–21089.
- 8.
Huang, L.; Liu, Z.; Luo, X.; et al. A Semi-supervised Approach with Dual Filters for Graph-Based Fraud Detection. In Machine Learning and Knowledge Engineering for Decision Making; Springer Nature Singapore: Singapore, 2027;
pp. 37–50. - 9.
Liu, Z.; Dou, Y.; Yu, P.S.; et al. Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, 25–30 July 2020; pp. 1569–1572.
- 10.
Jin, D.; Liu, Z.; Li, W.; et al. Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 February 2019; Volume 33, pp. 152–159.
- 11.
Liu, S.; He, D.; Yu, Z.; et al. Beyond Homophily: Neighborhood Distribution-Guided Graph Convolutional Networks. Expert Syst. Appl. 2025, 259, 125274.
- 12.
Shan, L.; Wang, N.; Zhao, J.; et al. Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing. arXiv 2026, arXiv:2606.10284.
- 13.
Zhu, J.; Yan, Y.; Zhao, L.; et al. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Adv. Neural Inf. Process. Syst. 2020, 33, 7793–7804.
- 14.
Dou, Y.; Liu, Z.; Sun, L.; et al. Enhancing Graph Neural Network-Based Fraud Detectors Against Camouflaged Fraudsters. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Online, 19–23 October 2020; pp. 315–324.
- 15.
Liu, Y.; Ao, X.; Qin, Z.; et al. Pick and Choose: A GNN-Based Imbalanced Learning Approach for Fraud Detection. In Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19–23 April 2021; pp. 3168–3177.
- 16.
Chai, Z.; You, S.; Yang, Y.; et al. Can Abnormality Be Detected by Graph Neural Networks? In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria, 23–29 July 2022; pp. 1945–1951.
- 17.
Zhuo, W.; Liu, Z.; Hooi, B.; et al. Partitioning Message Passing for Graph Fraud Detection. In Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria, 7–11 May 2024.
- 18.
Chen, N.; Liu, Z.; Hooi, B.; et al. Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision. In Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria, 7–11 May 2024.
- 19.
Liu, Z.; Yu, H.; Luo, X. A Semi-supervised Fraud Detection Model Based on Fuzzy Graph Neural Networks. Appl. Soft Comput. 2026, 202, 115913.
- 20.
Liu, S.; He, D.; Yu, Z.; et al. Integrating Co-Training with Edge Discrimination to Enhance Graph Neural Networks Under Heterophily. In Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA, 25 February–4 March 2025; Volume 39, pp. 18960–18968.
- 21.
Yu, Z.; Liang, C.; Chang, X.; et al. Dynamic Neighborhood Modeling via Node-Subgraph Contrastive Learning for Graph-Based Fraud Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA, 25 February–4 March 2025; Volume 39, pp. 13115–13123.
- 22.
Shao, Z.; Yu, H.; Wen, J.; et al. A Graph Fraud Detection Model Based on Mutual Information. Neurocomputing 2025, 663, 131972.
- 23.
Yu, Z.; Jin, D.; He, D.; et al. Integrated Mixture of Neighborhood and Community Experts for Graph-Based Fraud Detection. In Proceedings of the ACM Web Conference 2026, Dubai, United Arab Emirates, 13–17 April 2026; pp. 604–613.
- 24.
Liu, Z.; Gao, J.; Yu, H.; et al. A Robust Graph Fraud Detection Model Based on Adversarial Reweighting. IEEE Trans. Comput. Soc. Syst. 2025, 12, 5213–5224.
- 25.
Yang, Q.; Yu, H.; Liu, Z.; et al. Synthesizing Global and Local Perspectives in Contrastive Learning for Graph Anomaly Detection. Knowl.-Based Syst. 2025, 315, 113289.
- 26.
Liu, Z.; Gao, J.; Liao, Y.; et al. Knowledge-Enhanced Consistency Learning with Disentangled Multimodal Representation for Misinformation Detection. Inf. Fusion 2026, 136, 104501.
- 27.
Liu, Z.; Yu, H.; Liao, Y.; et al. Fuzzy Federated Graph Learning via TSK Message Passing and Contrastive Uncertainty Learning. IEEE Trans. Fuzzy Syst. 2026, 1–14. https://doi.org/10.1109/TFUZZ.2026.3713494.
- 28.
Liu, Z.; Chen, C.; Li, L.; et al. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. In Proceedings of the AAAI Conference on Artificial Intelligence; Honolulu, HI, USA, 27 January–1 February 2019; Volume 33, pp. 4424–4431.
- 29.
Li, A.; Qin, Z.; Liu, R.; et al. Spam Review Detection with Graph Convolutional Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Honolulu, HI, USA, 27 January–1 February 2019; pp. 2703–2711.
- 30.
Jiang, N.; Duan, F.; Chen, H.; et al. MAFI: GNN-Based Multiple Aggregators and Feature Interactions Network for Fraud Detection over Heterogeneous Graph. IEEE Trans. Big Data 2021, 8, 905–919.
- 31.
Zhang, G.; Wu, J.; Yang, J.; et al. FraudRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance. In Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, 7–10 December 2021; pp. 867–876.
- 32.
Xiang, S.; Zhu, M.; Cheng, D.; et al. Semi-Supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation. In Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA, 7–14 February 2023; pp. 14557–14565.
- 33.
Gao, Y.; Wang, X.; He, X.; et al. Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. In Proceedings of the ACM Web Conference 2023, Austin, TX, USA, 30 April–4 May 2023; pp. 1528–1538.
- 34.
Duan, M.; He, D.; Zheng, T.; et al. Global Attribute-Association Pattern Aggregation for Graph Fraud Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA, 25 February–4 March 2025; Volume 39, pp. 11616–11624.
- 35.
Wang, D.; Lin, J.; Cui, P.; et al. A Semi-Supervised Graph Attentive Network for Financial Fraud Detection. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China, 8–11 November 2019; pp. 598–607.
- 36.
Kumar, A.; Ghosh, S.; Verma, J. Guided Self-Training Based Semi-Supervised Learning for Fraud Detection. In Proceedings of the Third ACM International Conference on AI in Finance, New York, NY, USA, 2–4 November 2022; pp. 148–155.
- 37.
Lucas, T.; Weinzaepfel, P.; Rogez, G. Barely-Supervised Learning: Semi-Supervised Learning with Very Few Labeled Images. In Proceedings of the 36th AAAl Conferenceon Artificial Intelligence, Vancouver, BC, Canada, 22 February–1 March 2022; pp. 1881–1889.
- 38.
Gui, G.; Zhao, Z.; Qi, L.; et al. Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class. Adv. Neural Inf. Process. Syst. 2022, 35, 19849–19860.
- 39.
Yu, H.; Liu, Z.; Luo, X. Barely Supervised Learning for Graph-Based Fraud Detection. In Proceedings of the 38th AAAl Conferenceon Artificial Intelligence, Vancouver, BC, Canada 20–27 February 2024; Volume 38, pp. 16548–16557.
- 40.
Dong, X.; Zhang, X.; Chen, L.; et al. SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels. arXiv 2025, arXiv:2502.03201.
- 41.
Wang, H.; Leskovec, J. Unifying Graph Convolutional Neural Networks and Label Propagation. arXiv 2020, arXiv:2002.06755.
- 42.
Wang, H.; Leskovec, J. Combining Graph Convolutional Neural Networks and Label Propagation. ACM Trans. Inf. Syst. 2021, 40, 1–27.
- 43.
Shuman, D.I.; Narang, S.K.; Frossard, P.; et al. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal Process. Mag. 2013, 30, 83–98.
- 44.
Zhang, H.; Cisse, M.; Dauphin, Y. N.; et al. mixup: Beyond Empirical Risk Minimization. In Proceedings of the 6th International Conference on Learning Representations, Vancouver, BC, Canada, 30 April–3 May 2018.
- 45.
Luan, S.; Hua, C.; Lu, Q.; et al. Revisiting Heterophily for Graph Neural Networks. Adv. Neural Inf. Process. Syst. 2022, 35, 1362–1375.
- 46.
Kipf, T.N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. arXiv 2016, arXiv:1609.02907.
- 47.
Hamilton, W.; Ying, Z.; Leskovec, J. Inductive Representation Learning on Large Graphs. In Proceedings of the 31st International Conference on Neural Information Processing System, Red Hook, NY, USA, 4–9 December 2017; pp. 1025–1035.
- 48.
Velickovic, P.; Cucurull, G.; Casanova, A.; et al. Graph Attention Networks. arXiv 2017, arXiv:1710.10903.
- 49.
Fey, M.; Lenssen, J.E. Fast Graph Representation Learning with PyTorch Geometric. arXiv 2019, arXiv:1903.02428.
- 50.
Van der Maaten, L.; Hinton, G. Visualizing Data Using t-SNE. J. Mach. Learn. Res. 2008, 9, 2579–2605.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com