HCGBot: Learning Homophilous Context Graphs for Twitter Bot Detection
Wan, Herun Luo, Minnan Wang, Jihong Chang, Xiaojun Zheng, Qinghua
Wan, Herun
Luo, Minnan
Wang, Jihong
Chang, Xiaojun
Zheng, Qinghua
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Computer Vision
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Journal article
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English
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Abstract
Graph-based Twitter bot detectors are proven more effective than feature-based and text-based. Mainstream detectors only employ friend relationships, bringing two limitations: (I) friend relationships are sparse, ignoring implicit interactions between users, and (II) bots would follow humans to expand their influence, challenging the homophily principle. This paper aims to learn a homophilous context graph containing implicit interactions, which faces two challenges: (I) existing homophily measures are influenced by the class distribution, which is not suitable for the class imbalance situation of bot detection, and (II) existing graph learning paradigm would introduce noisy neighbors and consume computing resources. To this end, we first propose a class-independent homophily measure, which is proven to be robust to class distribution. Meanwhile, we propose HCGBot, which transforms graph learning into similarity metric learning. HCGBot contains a neighbor-mask GNN layer, which masks users that hardly implicitly interact and extracts topology and weight information from the context graph. Finally, we design a hybrid loss to optimize HCGBot, which maximizes the class-independent homophily measure while detecting bots. Extensive experiments prove that HCGBot achieves the best performance and learns a more homophilous context graph with high efficiency. Further analysis illustrates that HCGBot can detect social bots in more realistic situations.
Citation
H. Wan, M. Luo, J. Wang, X. Chang, Q. Zheng, "HCGBot: Learning Homophilous Context Graphs for Twitter Bot Detection," IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-13, 2026, https://doi.org/10.1109/tkde.2026.3656720.
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IEEE Transactions on Knowledge and Data Engineering
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46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, 4605 Data Management and Data Science
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IEEE