Addressing Noise and Stochasticity in Fraud Detection for Service Networks

TL;DR

This paper introduces SGNN-IB, a spectral graph network that effectively addresses noise and frequency-specific signal distortion in fraud detection for service networks, outperforming existing methods.

Contribution

The paper proposes a novel spectral graph network using information bottleneck and prototype learning to improve fraud detection by handling noise and preserving signal characteristics.

Findings

SGNN-IB outperforms state-of-the-art methods on real-world datasets.

Splitting graphs into homophilic and heterophilic subgraphs improves signal capture.

Using information bottleneck enhances the extraction of key features.

Abstract

Fraud detection is crucial in social service networks to maintain user trust and improve service network security. Existing spectral graph-based methods address this challenge by leveraging different graph filters to capture signals with different frequencies in service networks. However, most graph filter-based methods struggle with deriving clean and discriminative graph signals. On the one hand, they overlook the noise in the information propagation process, resulting in degradation of filtering ability. On the other hand, they fail to discriminate the frequency-specific characteristics of graph signals, leading to distortion of signals fusion. To address these issues, we develop a novel spectral graph network based on information bottleneck theory (SGNN-IB) for fraud detection in service networks. SGNN-IB splits the original graph into homophilic and heterophilic subgraphs to better capture the signals at different frequencies. For the first limitation, SGNN-IB applies information bottleneck theory to extract key characteristics of encoded representations. For the second limitation, SGNN-IB introduces prototype learning to implement signal fusion, preserving the frequency-specific characteristics of signals. Extensive experiments on three real-world datasets demonstrate that SGNN-IB outperforms state-of-the-art fraud detection methods.