EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph

Abstract

Fraud detection is extremely critical for e-commerce business platforms. Utilizing graph structure data and identifying unexpected dense subgraphs as suspicious is a category of commonly used fraud detection methods. Among them, spectral methods solve the problem efficiently but hurt the performance due to the relaxed constraints. Heuristic methods cannot be accelerated with parallel computation and fail to control the scope of returned suspicious nodes. These drawbacks affect the real-world applications of existing graph-based methods. In this paper, we propose an Ensemble based Fraud DETection (ENSEMFDET) method to scale up fraud detection in bipartite graphs. By oversampling the graph and solving the subproblems, the ensemble approach further votes suspicious nodes without sacrificing the prediction accuracy. Extensive experiments have been done on real transaction data from JD.com and demonstrate the effectiveness, practicability, and scalability of ENSEMFDET.