Detecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method

TL;DR

This paper introduces DS-DGA-GCN, an adaptive graph learning model that effectively detects fake reviewer groups in dynamic networks by modeling complex relationships and incorporating a novel attention mechanism, outperforming existing methods on real datasets.

Contribution

The paper proposes a novel adaptive graph learning model, DS-DGA-GCN, that enhances fake reviewer group detection by modeling joint relationships and integrating dynamic attention mechanisms.

Findings

Achieves up to 89.8% accuracy on Amazon dataset.

Outperforms state-of-the-art baselines in detection accuracy.

Effectively models temporal and structural features of reviewer networks.

Abstract

The proliferation of fake reviews, often produced by organized groups, undermines consumer trust and fair competition on online platforms. These groups employ sophisticated strategies that evade traditional detection methods, particularly in cold-start scenarios involving newly launched products with sparse data. To address this, we propose the \underline{D}iversity- and \underline{S}imilarity-aware \underline{D}ynamic \underline{G}raph \underline{A}ttention-enhanced \underline{G}raph \underline{C}onvolutional \underline{N}etwork (DS-DGA-GCN), a new graph learning model for detecting fake reviewer groups. DS-DGA-GCN achieves robust detection since it focuses on the joint relationships among products, reviews, and reviewers by modeling product-review-reviewer networks. DS-DGA-GCN also achieves adaptive detection by integrating a Network Feature Scoring (NFS) system and a new dynamic graph attention mechanism. The NFS system quantifies network attributes, including neighbor diversity, network self-similarity, as a unified feature score. The dynamic graph attention mechanism improves the adaptability and computational efficiency by captures features related to temporal information, node importance, and global network structure. Extensive experiments conducted on two real-world datasets derived from Amazon and Xiaohongshu demonstrate that DS-DGA-GCN significantly outperforms state-of-the-art baselines, achieving accuracies of up to \textbf{89.8\% and 88.3\%}, respectively.