Detecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method
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 amo...
| Authors | Jing Zhang, Ke Huang, Yao Zhang et al. |
|---|---|
| Primary category | cs.SI |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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