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SCN_GNN: A GNN-based fraud detection algorithm combining strong node and graph topology information
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DOI：10.1016/j.eswa.2023.121643.png)
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Abstract
En 中文
Graph neural networks (GNNs) have exhibited remarkable success in fraud detection. However, detecting fraud in datasets with scattered graphic densities and multiple relations remains challenging. This is a prevalent issue in fraud detection as fraudsters often employ diverse relationship types to camouflage their activities. Moreover, the constrained interconnectivity between nodes contributes to a scarcity of informative data, thereby intensifying the influence of raw features and further compounding the difficulties in fraud detection processes. To address these challenges, we present SCN_GNN (Strongly Connected Nodes-Graph Neural Network), a novel algorithm for fraud detection, that proposes two node sampling strategies based on the fusion of strong node information and graph topology information. Among them, the structured similarity-aware module (SSAM) performs up-sampling to add useful nodes to the sparse graph, while the strong node module (SNM) performs down-sampling based on strong node information and original features. Furthermore, we also reconfigure the RSRL (Recursive and Scalable Reinforcement Learning framework) module to improve fraud detection performance by increasing inter-class distances and decreasing intra-class distances, resulting in a refined decision boundary for optimized algorithmic efficacy. We use three metrics (AUC, recall, and G_Mean) to evaluate the performance of SCN_GNN. The experimental results compared with the state-of-the-art models on two real-world datasets demonstrate the superiority of the proposed SCN_GNN.
Keywords:
Camouflage
Fraud detection
Graph neural network
Graph topology information
Sampling strategy
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7.5
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2.9W
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10.2W
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