Top-k Structure Holes Detection Algorithm in Social Network

In the research of social networks, the structure holes usually refers to the vertices in the network at the key positions of information diffusion. The detection of such vertices is of great significance for the control of network public opinion, the analysis of the influence of social network, the detection of the weak points of the network security, the rapid spread of the information and so on. How to find more accurate structural holes has become a key problem in the research. In this paper, we proposed a method to detect the top-k structure holes based on the shortest path increment. It mainly through the analysis of SPIG (the shortest path increment), NCC (the number of sub connected components) and VAR (the variance of the vertices in a connected component) to determine the structure hole attribute values of the vertices. And then the vertices are sorted according to this value and obtained the top-k vertices. Our experiments on real networks and different scales LFR simulation complex networks clearly verify the effectiveness of our method, as well as the advantage of our method against the other similar methods.