HDN-GFD: Hypergraph neural network with dynamic neighborhood aggregation for graph-based camouflaged fraud detection

Abstract

Graph-based fraud detection, which identifies fraudulent and benign entities on graph-structured data, has shown strong potential in combating sophisticated fraud and attracted growing research attention. However, existing methods face two critical bottlenecks. First, increasingly complex fraud camouflage: fraudsters conceal collusive behaviors via multi-hop connections and deliberately link to benign nodes, preventing traditional models from capturing high-order patterns and causing feature homogenization of fraud nodes. Second, severe class imbalance: fraud nodes account for a tiny proportion of the graph, and weak fraud signals are easily overwhelmed by massive benign node information. To address these challenges, we propose HDN-GFD, a novel fraud detection framework integrating high-order hypergraph modeling and dynamic neighborhood aggregation. Specifically, we design a dual-dimensional hypergraph construction mechanism that upgrades pairwise connections to multi-node collaborative associations along structural and feature dimensions to capture high-order collusive relationships. We then develop an anomaly probability-guided dynamic aggregation strategy, which estimates node anomaly scores via node-subgraph feature consistency and adaptively aggregates neighborhood information from benign and fraudulent perspectives. This design decouples camouflage-induced confounding signals and amplifies minority fraud features, mitigating the adverse impact of class imbalance. Extensive experiments on four real-world datasets demonstrate that HDN-GFD consistently outperforms state-of-the-art baselines, verifying the effectiveness and superiority of our method.