Fraud Detection in Online Product Review Systems via Heterogeneous Graph Transformer

Abstract

In online product review systems, users are allowed to submit reviews about their purchased items or services. However, fake reviews posted by fraudulent users often mislead consumers and bring losses to enterprises. Traditional fraud detection algorithm mainly utilizes rule-based methods, which is insufficient for the rich user interactions and graph-structured data. In recent years, graph-based methods have been proposed to handle this situation, but few prior works have noticed the camouflage fraudster's behavior and inconsistency heterogeneous nature. Existing methods have either not addressed these two problems or only partially, which results in poor performance. Alternatively, we propose a new model named Fraud Aware Heterogeneous Graph Transformer(FAHGT), to address camouflages and inconsistency problems in a unified manner. FAHGT adopts a type-aware feature mapping mechanism to handle heterogeneous graph data, then implementing various relation scoring methods to alleviate inconsistency and discover camouflage. Finally, the neighbors' features are aggregated together to build an informative representation. Experimental results on different types of real-world datasets demonstrate that FAHGT outperforms the state-of-the-art baselines.

Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning

“…Tang et al [8] considered the disguised behavior of fraudsters and the inconsistency of heterogeneous graphs and proposed a heterogeneous graph-based fraud detection model FAHGT to detect fake users in e-commerce online review systems. The model injects domain knowledge through a relational scoring mechanism without hand-designed metapaths, and a type-aware feature mapping mechanism is used to process the heterogeneous graph data to mitigate inconsistency and discover fakes.…”

Evaluation Metrics for Overlapping Community Detection

“…Its main objective is understanding individual behaviors, based on their interactions. Network analysis has attracted significant interest due to its potential to handle many real-world case studies [1], [2]. In particular, community detection has become a fundamental and highly relevant research area in network science [3].…”