HyperRole: Hyperbolic Graph Transformer for Role Discovery in Online Social Networks

Huijun Tang, Ming Du, Pengfei Jiao, Huaming Wu, Zhidong Zhao

Abstract: Role discovery assist in various applications of online social networks, such as water army detection, shopping recommendation, rumor tracing, etc. However, existing studies often overlook the significance of hierarchical structures in online social networks, which are crucial for understanding the roles played by different users. To address this gap, we propose a novel approach based on hyperbolic graph learning, called HyperRole, which effectively leverages the hierarchical structure of online social networks for role discovery. HyperRole first extracts structural features from users and constructs user sequences based on feature similarity, capturing the relationships between users across different scales. Then, we learn role information from structural features by hyperbolic graph Transformer to embed users into the hyperbolic space, preserving the hierarchical structure between users and enabling interactions between users of the same level that are far away from each other. Additionally, we leverage the hierarchical distance between the target user and other users within the same sequence to guide and modify the role information of the target user. Based on the generated user role embeddings, we train a multi-class classifier to classify roles. Extensive experiments on several real-world network datasets demonstrate that our model outperforms existing baseline methods, showcasing its superior performance.

External IDs:dblp:conf/infocom/TangDJWZ25