F2GNN: An Adaptive Filter with Feature Segmentation for Graph-Based Fraud Detection

Abstract

Graph Neural Networks (GNNs) have received remarkable success in identifying fraudulent activities on graphs. Most approaches leverage the full user feature together and aggregate the messages from its neighbors by a graph filter. However, due to the adversarial activities like the camouflage of fraudsters, most dimensions of fraudsters' features resemble normal users, and modeling the features as a whole cannot fully explore the small-portion fraudulent features. In this paper, we attempt to segment the user features and apply adaptive graph filters on each segmentation for better modeling of fraudulent features. We propose an adaptive filter with feature segmentation (shortened as F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GNN) to alleviate these challenges. Experimental results on two real-world datasets demonstrate that F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GNN outperforms state-of-the-art baselines for graph-based fraud detection. In addition, the adaptive filter with feature segmentation can effectively address the class imbalance problem in the task of fraud detection.