RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks

In e-commerce, fake reviewers frequently post fake reviews to mislead consumers into making unwise shopping decisions, seriously affecting customers' benefits. Graph neural networks (GNNs) have been widely cultivated in fake reviewer detection in recent years, but they exhibit two main limitations. Firstly, most approaches model reviewer detection with a homogeneous graph, ignoring the interdependent clues among reviewers, reviews, and products, making them ineffective against stealthy fake reviewers. Secondly, few works noticed the camouflage behavior of fake reviewers, i.e., fake reviewers disguise themselves as authentic ones by establishing links with genuine reviewers, which extremely weakens the suspiciousness of fake reviewers and undermines detection performance. This paper proposes RHGNN, a robust fake reviewers detection framework based on Reinforced Heterogeneous Graph Neural Networks. Particularly, we first model fake reviewer detection with a heterogeneous graph consisting of reviews, reviewers, and products, to capture stealthy fraud clues by investigating their heterogeneous interactive relations. Subsequently, we present a reinforced neighbor selector (RNS) to sample and aggregate top−k informative neighbors while filtering out the camouflaged relationships. Finally, we propose a self-supervised heterogeneous graph embedding method based on mutual information to learn the discriminative features of fake reviewers. Experimental results show that RHGNN demonstrates promising and robust performance, especially in detecting well-disguised fake reviewers.