Open Access

ARTICLE

TopoMSG: A Topology-Aware Multi-Scale Graph Network for Social Bot Detection

Junhui Xu¹, Qi Wang^1,*, Chichen Lin², Weijian Fan³

1 School of Computer and Cyber Sciences, Communication University of China, Beijing, 100024, China
2 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China
3 School of Data Science and Intelligent Media, Communication University of China, Beijing, 100024, China

* Corresponding Author: Qi Wang. Email:

Computers, Materials & Continua 2026, 86(3), 47 https://doi.org/10.32604/cmc.2025.071661

Received 09 August 2025; Accepted 28 October 2025; Issue published 12 January 2026

Abstract

Social bots are automated programs designed to spread rumors and misinformation, posing significant threats to online security. Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots: a higher number of connected components weakens their collaborative capabilities, thereby reducing their proportion within the overall network. However, current social bot detection methods still make limited use of topological features. Furthermore, both graph neural network (GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations, and existing studies lack an effective fusion of multi-scale information. To address these issues, this paper proposes a topology-aware multi-scale social bot detection method, which jointly learns local and global representations through a co-training mechanism. At the local level, topological features are effectively embedded into node representations, enhancing expressiveness while alleviating the over-smoothing problem in GNNs. At the global level, a clustering attention mechanism is introduced to learn global node representations, mitigating the over-globalization problem. Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches. Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/ (accessed on 27 October 2025).

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Keywords

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