SparseFraudNet: A Graph-based Approach for Cold-start Fraud Detection with Information Aggregation

Online reviews play a critical role in influencing consumer’s purchasing decision on e-commerce, making them a prime target for manipulation through fraudulent reviews. Although various Fraud Detection (FD) techniques have been presented, a crucial problem still remains unaddressed, i.e., the cold-start problem in FD, which refers to the difficulty in identifying fraudulent reviews due to limited historical data for new users and new products. Existing graph-based detection methods, while effective for well-connected nodes, are suffering Sparse Graph (SG) connections in cold-start FD. In this paper, we propose a novel approach called SparseFraudNet to address the problem of cold-start FD with information aggregation. Specifically, the local information aggregation is proposed to dynamically optimize neighbor selection using Reinforcement Learning (RL) with Bernoulli Multi-Armed Bandit (BMAB), with the goal to capture the five key types of relations among reviews. The global information aggregation is proposed to leverage Graph Coarsening (GC) with manifold learning and spectral clustering to mitigate adjacency matrix sparsity for new users under new products using Sparse Spectral Clustering (SSC). Experiments on the YelpZip-Cold and YelpNYC-Cold datasets demonstrate that the proposed SparseFraudNet approach significantly outperforms state-of-the-art methods in FD in terms of accuracy, precision, recall, F1 measure and AUC to identify fraudulent reviews of new users under new products.