Predicting potential links via combined hybrid influence of endpoints and connectivity of quasi-local paths

Abstract

In complex networks, topological similarity based link prediction promotes the development of network science. Traditional researches consider that two unconnected endpoints have possibility to make a link if they possess large influence respectively. However, through profound investigations, we find that one endpoint with large influence also can attract other endpoints around. The phenomenon reveals that the mutual attractions between two unconnected endpoints depend on their combined influence instead of only single influence. Furthermore, previous researches pay more attention to the influence of endpoints with only degree considered. However, in quasi-local paths, we discover that synthesizing degree and H-index can more reliably capture the endpoints with great and extensive maximum connected subgraph, which can more possibly attract other unconnected endpoints. To sum up, we propose a model named combined hybrid influence connectivity index (CHIC) in this paper to explore its role on similarity based link prediction. The comparisons with eight mainstream indices are performed on experiments in twelve real data sets and the results show an improvement of prediction performance via CHIC index.