Nowhere to H2IDE: Fraud Detection From Multi-Relation Graphs via Disentangled Homophily and Heterophily Identification

Abstract

Fraud detection has always been one of the primary concerns in social and economic activities and is becoming a decisive force in the booming digital economy. Graph structures formed by rich user interactions naturally serve as important clues for identifying fraudsters. While numerous graph neural network-based methods have been proposed, the diverse interactive connections within graphs and the heterophilic connections deliberately established by fraudsters to normal users as camouflage pose new research challenges. In this light, we propose H²IDE (Homophily and Heterophily Identification with Disentangled Embeddings) for accurate fraud detection in multi-relation graphs. H²IDE features in an independence-constrained disentangled representation learning scheme to capture various latent behavioral patterns in graphs, along with a supervised identification task to specifically model the factor-wise heterophilic connections, both of which are proven crucial to fraud detection. We also design a relation-aware attention mechanism for hierarchical and adaptive neighborhood aggregation in H²IDE. Extensive comparative experiments with state-of-the-art baseline methods on two real-world multi-relation graphs and two large-scale homogeneous graphs demonstrate the superiority and scalability of our proposed method and highlight the key role of disentangled representation learning with homophily and heterophily identification.