Effective Link Prediction Based on Community Relationship Strength

Abstract

Link prediction is one of the research hotspots in complex network analysis and has a wide range of applications in both theory and reality. To improve the prediction accuracy, this paper proposes a new link prediction framework by considering both node similarity and community information, which overcomes the weaknesses of existing community-based prediction methods. In the proposed framework, a reasonable measure, called community relationship strength (CRS), is defined to estimate the closeness between communities. In this paper, we hold the view that the connection likelihood between two target nodes rests upon not only their similarity but also the closeness of communities that they belong to. Therefore, to measure the connection likelihood, the proposed framework combines CRS with traditional similarity indexes. Three CRS-based methods are derived from the framework. The performance of the CRS-based methods is comprehensively studied on 12 real-world networks compared with several groups of baselines. The experimental results indicate that the CRS-based methods are more effective and robust than others.