DeepHole: Identifying Structural Hole Spanners in Online Social Networks Using Behavior Embedding

Influential users in online social networks have been given a lot of attention, which could maximize information diffusion in a centralized dissemination process. However, there exist users that can significantly promote diversified information communication among users, which is a pluralistic interaction process. Structural hole (SH) spanners serve as a bridge between social groups, promoting interactions between users and allowing ordinary users to receive diverse information. Filling structural holes, SH spanners have more opportunities to obtain innovative content. Identifying SH spanners is challenging due to their inconspicuous characteristics and privacy policy restrictions. In this article, we are the first to study the SH spanner identification problem using the behavior data generated by users, releasing the dependency on the entire graph structure to find SH spanners. We propose DeepHole to design both the semantic and the sequential behavior analysis modules on users' generated content, utilizing the TextCNN and Transformer models, respectively. The semantic and sequential embeddings are effective in distinguishing the SH spanners from ordinary users. We conduct comprehensive evaluations using the Yelp and Foursquare datasets simultaneously. Results show that DeepHole can achieve a satisfying performance in detecting SH spanners labeled by both constraint and effective size metrics, with AUC values of 0.923 and 0.933 for Yelp and 0.828 and 0.815 for Foursquare.