Abstract
Detecting fraudulent nodes from topological graphs is important in many real applications, such as financial fraud detection. This task is challenging due to both the class imbalance issue and the camouflaged behaviors of anomalous nodes. Recently, some graph contrastive learning (GCL) methods have been proposed to solve the above issue. However, local aggregation-based GNN encoders can not consider the long-distance nodes, leading to over-smoothing and false negative samples. Also, random perturbation data augmentation hinders separately considering camouflaged behaviors at the topological and feature levels. To address that, this paper proposes a novel contrastive learning architecture for enhancing the performance of graph fraud detection. Specifically, a context generator and a representation refinement module are embraced for mitigating the limitation of local aggregation in finding long-distance fraudsters, as well as the introduction of false negative samples in GCL. Further, a multi-channel fusion module is designed to adaptively defend against diverse camouflaged behaviors. The experimental results on real-world datasets show a significant performance improvement over baselines, which demonstrates its effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bello, I.: LambdaNetworks: modeling long-range interactions without attention. In: International Conference on Learning Representations (2020)
Chen, B., et al.: GCCAD: graph contrastive coding for anomaly detection. arXiv: abs/2108.07516 (2021)
Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized PageRank graph neural network. In: International Conference on Learning Representations (2020)
Ding, K., Zhou, Q., Tong, H., Liu, H.: Few-shot network anomaly detection via cross-network meta-learning. In: Proceedings of the Web Conference (2021)
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 (2020)
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
Jiang, Z., et al.: Camouflaged Chinese spam content detection with semi-supervised generative active learning. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3080–3085 (2020)
Khosla, P., et al.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 18661–18673 (2020)
Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv: abs/1609.02907 (2017)
Kreuzer, D., Beaini, D., Hamilton, W.L., L’etourneau, V., Tossou, P.: Rethinking graph transformers with spectral attention. In: Advances in Neural Information Processing Systems, vol. 34 (2021)
Li, A., Qin, Z., Liu, R., Yang, Y., Li, D.: Spam review detection with graph convolutional networks. In: Proceedings of the 28th ACM International Conference on Information & Knowledge Management (2019)
Liang, C., et al.: Uncovering insurance fraud conspiracy with network learning. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)
Liu, Y., et al.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In: Proceedings of the Web Conference (2021)
Liu, Y., Ao, X., Zhong, Q., Feng, J., Tang, J., He, Q.: Alike and unlike: resolving class imbalance problem in financial credit risk assessment. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)
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), 2378–2392 (2021)
Liu, Z., Dou, Y., Yu, P.S., Deng, Y., Peng, H.: Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: Proceedings of the 43nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)
Liu, Z., Chen, C., Li, L., Zhou, J., Li, X., Song, L.: GeniePath: graph neural networks with adaptive receptive paths. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (2019)
Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., Song, L.: Heterogeneous graph neural networks for malicious account detection. In: Proceedings of the 27th ACM International Conference on Information & Knowledge Management (2018)
Ma, X., Wu, J., Xue, S., Yang, J., Sheng, Q.Z., Xiong, H.: A comprehensive survey on graph anomaly detection with deep learning. IEEE Trans. Knowl. Data Eng. 35(12), 12012–12038 (2021)
Pei, H., Wei, B., Chang, K.C.C., Lei, Y., Yang, B.: Geom-GCN: geometric graph convolutional networks. In: International Conference on Learning Representations (2020)
Ren, Y., Wang, B., Zhang, J., Chang, Y.: Adversarial active learning based heterogeneous graph neural network for fake news detection. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 452–461 (2020)
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., Isola, P.: What makes for good views for contrastive learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6827–6839 (2020)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio’, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Velickovic, P., Fedus, W., Hamilton, W.L., Lio’, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: International Conference on Learning Representations (2018)
Wang, D., et al.: A semi-supervised graph attentive network for financial fraud detection. In: IEEE International Conference on Data Mining (ICDM), pp. 598–607 (2019)
Wang, X., Zhu, M., Bo, D., Cui, P., Shi, C., Pei, J.: AM-GCN: adaptive multi-channel graph convolutional networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)
Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., Chen, H.: Decoupling representation learning and classification for GNN-based anomaly detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
Yang, X., Lyu, Y., Tian, T., Liu, Y., Liu, Y., Zhang, X.: Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 1417–1423 (2021)
Zhang, G., et al.: FRAUDRE: fraud detection dual-resistant to graph inconsistency and imbalance. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 867–876 (2021)
Zhang, Y., Fan, Y., Ye, Y., Zhao, L., Shi, C.: Key player identification in underground forums over attributed heterogeneous information network embedding framework. In: Proceedings of the 28th ACM International Conference on Information & Knowledge Management (CIKM 2019), pp. 549–558 (2019)
Zhao, T., Ni, B., Yu, W., Guo, Z., Shah, N., Jiang, M.: Action sequence augmentation for early graph-based anomaly detection. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, G., Tang, D., Shatsila, A., Zhang, X. (2024). MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_3
Download citation
DOI: https://doi.org/10.1007/978-981-97-2421-5_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2420-8
Online ISBN: 978-981-97-2421-5
eBook Packages: Computer ScienceComputer Science (R0)