Link prediction based on the powerful combination of endpoints and neighbors

Abstract

Performance improvement of topological similarity-based link prediction models becomes an important research in complex networks. In the models based on node influence, researchers mainly consider the roles of endpoints or neighbors. Through investigations, we find that an endpoint with large influence has many neighbors. Meanwhile, the neighbors connect with more nodes besides endpoint, meaning that the endpoint can transmit extensive influence by the powerful combination of itself and neighbors. In addition, we evaluate the node influence by degree because the degree represents the number of neighbors accurately. In this paper, through focusing on the degree of endpoints and neighbors, we propose the powerful combination of endpoints and neighbors (PCEN) model. Experiments on twelve real network datasets demonstrate that the proposed model has better prediction performances than the traditional models.