CAGCL: A Community-Aware Graph Contrastive Learning Model for Social Bot Detection

Malicious social bot detection is vital for social network security. While graph neural networks (GNNs) based methods have improved performance by modeling structural information, they often overlook latent community structures, resulting in homogeneous node representations. Leveraging community structures, which capture discriminative group-level patterns, is therefore essential for more robust detection. In this paper, we propose a new Community-Aware Graph Contrastive Learning (CAGCL) framework for enhanced social bot detection. Specifically, CAGCL first exploits the latent community structures to uncover the potential group-level patterns. Then, a dual-perspective community enhancement module is proposed, which strengthens the structural awareness and reinforces topological consistency within communities, thereby enabling more distinctive node representations and deeper intra-community message passing. Finally, a community-aware contrastive learning module is proposed, which considers nodes within the same community as positive pairs and those from different communities as negative pairs, enhancing the discriminability of node representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that CAGCL consistently outperforms state-of-the-art baselines. The code is available at https://github.com/cgao-comp/.