Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection

TL;DR

This paper introduces a novel cost-sensitive graph neural network (CSGNN) to address class imbalance in mobile social network fraud detection, significantly improving detection accuracy on real-world datasets.

Contribution

The paper proposes a new CSGNN model that combines cost-sensitive learning with GNNs to effectively handle class imbalance in fraud detection tasks.

Findings

CSGNN outperforms existing algorithms on real-world datasets.

The method effectively addresses the graph imbalance problem.

Experimental results demonstrate improved detection performance.

Abstract

With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work. In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks. We conduct extensive experiments on two open-source realworld mobile network fraud datasets. The results show that CSGNN can effectively solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms. We believe that our research can be applied to solve the graph imbalance problems in other fields. The CSGNN code and datasets are publicly available at https://github.com/xxhu94/CSGNN.