Finding reinforced structural hole spanners in social networks via node embedding

Abstract

Identifying structural hole spanners that benefit from acting as bridges between communities is a core study in social network analysis. Existing methods for identification mainly focus on measuring the ability of users to control information propagation by bridging holes, while ignoring the impact of reinforcement of the holes themselves on the benefits of bridging spanners. A recent sociological study shows that the more reinforced a hole is, the more likely it is to bring high benefits to its spanners. In this paper, we propose a node embedding-based method ReHSe for identifying reinforced structural hole spanners in social networks. Specifically, an integrated embedding method is devised to extract features encoding reinforcement properties of nodes into a low-dimensional space. Further, to improve the robustness and accuracy of identification, an incremental learning strategy based on a reserved set is employed to train a scoring network in this subspace, to find top- $k$ reinforced hole spanners. Extensive experimental results show that the performance of hole spanners identified by the proposed method outperforms several existing methods.