HGT-FD: Hypergraph transformer for Fraud Detection

Graph-based fraud detection aims to identify anomalous patterns or fraudulent behaviors in graph-structured data, playing a crucial role across various domains. However, traditional models primarily focus on simple node-to-node message passing, which limits the integration of multi-node features and overlooks long-range dependencies in fraud detection. Hyperedge features in hypergraphs, as an integration of node features, can convey and aggregate multi-node characteristics, providing environmental information for the model to detect fraudulent nodes. To this end, we propose HGT-FD for hypergraph-based fraud detection. Specifically, we design a Hypergraph Transformer model that can directly employ hyperedge features for fraud detection, utilizing a co-attention mechanism to generate node representations. Furthermore, structural encoding and positional encoding are proposed to enhance the model's perception of hypergraph structures, enabling the model to capture more complex high-order structural relationships. Extensive experimental results on three fraud detection datasets demonstrate that the proposed method exhibits significant advantages over baselines in fraud detection.