Identifying Top- k Structural Hole Spanners in Large-Scale Social Networks

Recent studies have shown that in social networks, users who bridge different communities, known as structural hole spanners, have great potentials to acquire available resources from these communities and gain access to multiple sources of information flow. Structural hole spanners are crucial in many applications such as community detections, diffusion controls, and viral marketing. In spite of their importance, not much attention has been paid to them. Particularly, how to characterize the structural hole spanner properties and how to devise efficient yet scalable algorithms to find them are fundamental issues. In this paper, we formulate the problem as the top-k structural hole spanner problem. Specifically, we first provide a generic model to measure the quality of structural hole spanners, by exploring their properties, and show that the problem is NP-hard. We then devise efficient and scalable algorithms, by exploiting the bounded inverse closeness centralities of vertices and making use of articulation points of the network. We finally evaluate the performance of the proposed algorithms through extensive experiments on real and synthetic datasets, and validate the effectiveness of the proposed model. Our experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the proposed algorithms are very promising.