Detecting collusive fake reviews using heterogeneous graph attention networks

Yu Ning; Lujie Li

doi:10.1117/12.3038163

22 August 2024 Detecting collusive fake reviews using heterogeneous graph attention networks

Yu Ning, Lujie Li

Author Affiliations +

Proceedings Volume 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024); 132281Q (2024) https://doi.org/10.1117/12.3038163
Event: Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 2024, Guangzhou, China

Abstract

Given that heterogeneous information networks contain richer information and more complex semantics compared to homogeneous graphs, this paper utilizes heterogeneous graphs to depict the diverse relationships among users, reviews, and stores within a fake review dataset, thereby more effectively revealing the associations between review publishers and their reviews. This structural advantage facilitates the detection of collusive behaviors among fake review groups and captures global features. Moreover, we employ a Heterogeneous graph Attention Network (HAN) for automatic feature extraction of reviewer nodes. Within this framework, node-level attention learns the interactions between nodes and their neighbors defined by meta-paths, while semantic-level attention focuses on assessing the importance of different metapaths in the heterogeneous graph for specific tasks. Through the learning of these two levels of attention, our model can hierarchically optimize the combination of neighbors and meta-paths, resulting in node embeddings that more accurately capture the complex structures and rich semantics within the heterogeneous graph. Based on these advanced features, our approach effectively detects and identifies collusive fake reviews, demonstrating superior performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Ning and Lujie Li "Detecting collusive fake reviews using heterogeneous graph attention networks", Proc. SPIE 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 132281Q (22 August 2024); https://doi.org/10.1117/12.3038163

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