Abstract
The previous graph neural network-based fraud detection techniques were usually realized by clustering the neighbors with different relationships. However, the graph-based datasets face the issues of imbalanced features, classifications, and relationships, which directly decreases the detection performance. In this case, this work proposes a novel real-time GNN model to address this issue. Firstly, the features are measured to find the entities which have the highest similarity to the fraudster. The entities are sampled to identify the fraudsters in training. The fraudsters are far less than the normal nodes in the dataset. We then combine the Under-Sampling algorithm and the long-distance sampling algorithm to find the nodes that are similar to the neighbors. Finally, a reinforcement learning (RL)-based reward and punishment mechanism is proposed for sampling the weight between the relationships. It is effective to the issue of imbalanced relationships in the graph-based dataset. Experiments show that the proposed technique is superior to the comparative models on the real-world fraud dataset.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (Nos. 62072170,61902167,61872138), the Hunan Provincial Science & Technology Project Foundation (No. 2018TP1018), the Natural Science Foundation of Hunan Province, China (No. 2020JJ5369), the General Project of Education Department of Hunan Province, China (No. 19C1157), the Science Foundation of the Fujian Province, China (No. 2021J011015), Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201920) and Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF-IPIC202008).
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Long, J., Fang, F., Luo, H. (2022). A Novel GNN Model for Fraud Detection in Online Trading Activities. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_40
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