CGNN
A Compatibility-Aware Graph Neural Network for Social Media Bot Detection
Bibliographic Data
| ID | 22107403 |
|---|---|
| Authors | Haitao Huang (0000-0002-3861-2702, Chinese Academy of Sciences), Hu Tian (0000-0002-3966-2779, Peking University), Xiaolong Zheng (0000-0001-7950-6773, Chinese Academy of Sciences), Xingwei Zhang (0000-0002-0837-9890, Chinese Academy of Sciences), Daniel Dajun Zeng (0000-0002-9046-222X, Chinese Academy of Sciences), Fei‐yue Wang (0000-0001-9185-3989, Chinese Academy of Sciences) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 6528-6543 |
| Publication date | 2024-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3396413 |
| OpenAlex | W4399452222 |
| Language | EN |
| Citations received | 1 |
| References cited | 85 |
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 indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks
Artificial neural network · Computer security · Social media · World Wide Web · Advanced Malware Detection Techniques · Computer Science · Engineering · Network Security and Intrusion Detection · Spam and Phishing Detection · Artificial Intelligence
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |