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Abstract
Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler–Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.
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Details
1 Zhengzhou University, School of Cyber Science and Engineering, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846); Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, China (GRID:grid.207374.5)
2 Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, China (GRID:grid.207374.5)