FAKE REVIEW DETECTION USING GRAPH NEURAL NETWORKS ON LARGE-SCALE E-COMMERCE DATASETS

Abstract

The objective of this study is to develop a robust and scalable framework for detecting fake product reviews in large-scale e-commerce platforms using Graph Neural Networks (GNNs). The research aims to improve the accuracy of review authenticity classification by modeling user-product-review interactions as graph structures, capturing relational and behavioral patterns beyond traditional feature-based methods.The proposed approach constructs a heterogeneous graph from an e-commerce dataset where nodes represent users, products, and reviews, and edges encode their relationships. We apply a Graph Neural Network model that leverages both structural and semantic features extracted from the graph. Attention mechanisms are incorporated to focus on influential connections. The model is trained and evaluated on publicly available datasets such as Amazon and Yelp, with preprocessing steps including review vectorization, graph formation, and normalization. Benchmark comparisons are made against conventional machine learning classifiers and deep learning models. Experimental results demonstrate that the GNN-based model significantly outperforms baseline methods, achieving a higher F1-score, precision, and recall across multiple datasets. The graph-based approach exhibits superior ability to capture deceptive behavior patterns and uncover collusive review activities. The model also shows scalability when applied to datasets with millions of reviews.