Abstract
Recently, many fraud detection models introduced graph neural networks (GNNs) to improve the model performance. However, fraudsters often disguise themselves by camouflaging their features or relations. Due to the aggregation nature of GNNs, information from both input features and graph structure will be compressed for representation learning simultaneously. On the one hand, since not all neighbors provide useful information due to camouflage, aggregating information from all neighbors may potentially decrease the model performance. On the other hand, the structure including all neighbors is not reliable due to the relation camouflage. In this paper, we propose to decouple attribute learning and structure learning to avoid the mutual influence of feature and relation camouflage. Therefore, the model first learns its embedding seperately and then combine them together with label-guided contrastive losses to make predictions better. We conduct extensive experiments on two real-world datasets, and the results show the effectiveness of the proposed model.
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References
Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: AAAI (2020)
Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., Yu, P.S.: Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: CIKM, pp. 315–324 (2020)
Fu, X., Zhang, J., Meng, Z., King, I.: Magnn: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the Web Conference 2020, pp. 2331–2341 (2020)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In NeurIPS, pp. 1025–1035 (2017)
Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: Fraudar: bounding graph fraud in the face of camouflage. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Jiao, Y., Xiong, Y., Zhang, J., Zhang, Y., Zhang, T., Zhu, Y.: Sub-graph contrast for scalable self-supervised graph representation learning. In: ICDM, pp. 222–231. IEEE (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: ICML (2019)
Li, X., Wei, W., Feng, X., Liu, X., Zheng, Z.: Representation learning of graphs using graph convolutional multilayer networks based on motifs. Neurocomputing 464, 218–226 (2021)
Liang, T., et al.: Credit risk and limits forecasting in e-commerce consumer lending service via multi-view-aware mixture-of-experts nets. In: WSDM, pp. 229–237 (2021)
Liu, Y., et al.: Pick and choose: a gnn-based imbalanced learning approach for fraud detection. In: Proceedings of the Web Conference 2021, pp. 3168–3177 (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: SIGIR, pp. 1569–1572 (2020)
McAuley, J.J., Leskovec, J.: From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: WWW, pp. 897–908 (2013)
Meng, L., Zhang, J.: Isonn: isomorphic neural network for graph representation learning and classification. arXiv preprint arXiv:1907.09495 (2019)
Qiu, J., et al.: GCC: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1150–1160 (2020)
Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: SIGKDD (2015)
Ren, Y., Zhu, H., Zhang, J., Dai, P., Bo, L.: Ensemfdet: an ensemble approach to fraud detection based on bipartite graph. In: ICDE (2021)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Sun, Q., et al.: Sugar: subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism. In: WWW, pp. 2081–2091 (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Veličković, P., et al.: Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)
Wang, D., et al.: A semi-supervised graph attentive network for financial fraud detection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 598–607. IEEE (2019)
Wang, J., Wen, R., Wu, C., Huang, Y., Xion, J.: Fdgars: fraudster detection via graph convolutional networks in online app review system. In: WWW (2019)
Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., Chen, H.: Decoupling representation learning and classification for gnn-based anomaly detection. In: SIGIR, pp. 1239–1248 (2021)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2018)
Yang, C., Liu, M., Zheng, V.W., Han, J.: Node, motif and subgraph: leveraging network functional blocks through structural convolution. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 47–52. IEEE (2018)
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W.L., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 4805–4815 (2018)
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. Adv. Neural Inf. Process. Syst. 33, 5812–5823 (2020)
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. IEEE (2021)
Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: AAAI (2018)
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 and Knowledge Management, pp. 549–558 (2019)
Zhao, J., Wang, X., Shi, C., Binbin, H., Song, G., Ye, Y.: Heterogeneous graph structure learning for graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4697–4705 (2021)
Acknowledgement
This work is partially supported by NSF through grants IIS-1763365 and IIS-2106972.
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Meng, L., Ren, Y., Zhang, J. (2023). Decoupling Graph Neural Network with Contrastive Learning for Fraud Detection. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_30
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DOI: https://doi.org/10.1007/978-3-031-30678-5_30
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