CGNN: A Compatibility-Aware Graph Neural Network for Social Media Bot Detection

作者：Haitao Huang, Hu Tian, Xiaolong Zheng, Xingwei Zhang, Daniel Zeng, Fei–Yue Wang · 发表于：IEEE Transactions on Computational Social Systems · 年份：2024 · DOI：10.1109/tcss.2024.3396413 · 被引用次数：16 · 研究领域：Advanced Malware Detection Techniques、Spam and Phishing Detection、Network Security and Intrusion Detection

With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a compatibility-aware graph neural network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicate...