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

2026-03-09

AI Summary

This work proposes the DS-DGA-GCN model to address the challenge of detecting fake review groups under cold-start and data-sparse conditions for new products. The approach constructs a product-review-reviewer heterogeneous graph and introduces a novel unified scoring mechanism that integrates neighbor diversity and network self-similarity. Furthermore, it designs a dynamic graph attention mechanism that incorporates temporal information, node importance, and global structural cues to enable adaptive feature learning on dynamic heterogeneous graphs. Evaluated on real-world datasets from Amazon and Xiaohongshu, the model achieves accuracy rates of 89.8% and 88.3%, respectively, significantly outperforming current state-of-the-art methods.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• fake reviews
• reviewer groups
• cold-start
• dynamic networks
• online platforms

Innovation

• Dynamic Graph Attention
• Graph Neural Network
• Fake Review Detection
• Network Feature Scoring
• Cold-start Scenario