Predicting missing links via effective paths

Recently, in complex network, link prediction has brought a surge of researches, among which similarity based link prediction outstandingly gains considerable success, especially similarity in terms of paths. In investigation of paths based similarity, we find that the effective influence of endpoints and strong connectivity make paths contribute more similarity between two unconnected endpoints, leading to a more accurate link prediction. Accordingly, we propose a so-called effective path index (EP) in this paper to leverage effective influence of endpoints and strong connectivity in similarity calculation. For demonstrating excellence of our index, the comparisons with six mainstream indices are performed on experiments in 15 real datasets and results show a great improvement of performance via our index.