Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum

TL;DR

This paper introduces SEC-GFD, a semi-supervised GNN model designed for fraud detection on heterophilic graphs, addressing heterophily and label utilization issues to improve detection accuracy.

Contribution

The paper proposes a novel GNN-based fraud detector with spectral filtering and local environmental constraints to handle heterophily and enhance label utilization.

Findings

SEC-GFD outperforms existing fraud detection methods on real-world datasets.

The spectral filtering module effectively mitigates heterophily issues.

Adaptive local constraints improve label information utilization.

Abstract

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.