Imbalanced Fake Reviews?Detection with Ensemble Hierarchical Graph Attention Network

Abstract

As a hot spot in machine learning, graph neural networks (GNN) have recently begun to be applied in the field of fraud detection involving user reviews. In reality, the collected user comments involve diverse fields and complex information, and the fraud information in the massive user-generated content is usually in the minority, so that the GNN-based fraud detection methods are not ideal for this task. Aiming to solve the problems of heterogeneous features and uneven data distribution, a new ensemble hierarchical graph attention network (En-HGAN) detection method is proposed through modeling the review system as a heterogeneous network. The hierarchical attention is used to learn representations with richer semantics for comments by making full use of user behavior information in the heterogeneous network, and the Bagging framework introducing random under sampling is adopted to aggregate multiple discriminative HGAN sub-models, thereby reducing the effective information loss as well as enhancing the detection ability for fraud comments. Experimental results on YelpChi and Amazon real datasets show that this method has good anomaly detection performance. Compared with state-of-the- art methods, experimental results show that this method has nice robustness to deceptive entities when the data category is skewed.

Keywords

• fake review detection; hierarchical graph attention network; network representation learning; ensemble learning; imbalanced data classification