Graph-Based Fraud Detection with Dual-Path Graph Filtering

TL;DR

This paper introduces DPF-GFD, a dual-path graph filtering approach that enhances fraud detection on complex, heterophilous graphs by combining structural and similarity-based features.

Contribution

The paper proposes a novel dual-path filtering paradigm that explicitly decouples structural and feature similarity modeling for improved fraud detection in graphs.

Findings

Outperforms existing methods on four real-world datasets.

Effectively handles relation camouflage, heterophily, and class imbalance.

Provides more discriminative and stable node representations.

Abstract

Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural networks (GNNs) are naturally suited for processing information encoded in graph form through their message-passing operations, methods based on GNN models have increasingly attracted attention in the fraud detection domain. However, fraud graphs inherently exhibit relation camouflage, high heterophily, and class imbalance, causing most GNNs to underperform in fraud detection tasks. To address these challenges, this paper proposes a Graph-Based Fraud Detection Model with Dual-Path Graph Filtering (DPF-GFD). DPF-GFD first applies a beta wavelet-based operator to the original graph to capture key structural patterns. It then constructs a similarity graph from distance-based node representations and applies an improved low-pass filter. The embeddings from the original and similarity graphs are fused through supervised representation learning to obtain node features, which are finally used by an ensemble tree model to assess the fraud risk of unlabeled nodes. Unlike existing single-graph smoothing approaches, DPF-GFD introduces a frequency-complementary dual-path filtering paradigm tailored for fraud detection, explicitly decoupling structural anomaly modeling and feature similarity modeling. This design enables more discriminative and stable node representations in highly heterophilous and imbalanced fraud graphs. Comprehensive experiments on four real-world financial fraud detection datasets demonstrate the effectiveness of our proposed method.