No 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.

Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines.