Accurate similarity index based on the contributions of paths and end nodes for link prediction

Abstract

Link prediction whose intent is to discover the likelihood of the existence of a link between two disconnected nodes is an important task in complex network analysis. To perform this task, a similarity-based algorithm that employs the similarities of nodes to find links is a very popular solution. However, when calculating the similarity between two nodes, most of the similarity-based algorithms only focus on the contributions of paths connecting these two nodes but ignore the influences of these two nodes themselves. Therefore, their results are not accurate enough. In this paper, a novel similarity index, called Scop, is proposed for link prediction. By directly defining the contributions of paths to their end nodes and the contributions of end nodes themselves, Scop not only distinguishes the contributions of different paths but also integrates the contributions of end nodes. Hence, Scop can obtain better performance on accuracy. Experiments on 10 networks compared with six baselines indicate that Scop is remarkably better than others.