Detecting Social Bots via Multi-Motif Attention Fusion Network

The rapid growth of social networks has enabled the widespread deployment of social bots that manipulate public opinion and disseminate misinformation, thereby posing significant cybersecurity risks. Most existing detection methods for social bots primarily focus on individual features and low-order neighbor information, while neglecting the higher-order topological semantics embedded in frequent substructures, or motifs. This oversight limits their ability to effectively identify sophisticated social bots exhibiting complex behaviors. To address this gap, we propose a novel approach called the multi-motif attention fusion network (MMAFN). Our method enhances the ability to capture complex structures by explicitly modeling higher-order topological relationships within social graphs. Specifically, we first extract typical network motifs from the original graph to generate motif networks that preserve higher-order interaction patterns. We then integrate the original graph structure with these motif networks across multiple scales, constructing a composite adjacency relationship that captures multilevel semantics. Following this integration, we design specialized graph convolution operators that perform parallel feature propagation based on the composite adjacency matrix, generating node representations that incorporate multiorder topological information. Finally, we dynamically merge features from different motif views through an attention mechanism, establishing interaction channels between motifs and ultimately predicting the anomaly probability of user accounts. Experimental results on two large-scale datasets demonstrate that our method significantly outperforms state-of-the-art methods, exhibiting superior detection performance and robust generalization capabilities.