Hybrid influence of degree and H-index in the link prediction of complex networks

Abstract

Previous link prediction researchers paid more attention to the delivery ability of paths between two unlinked endpoints, but less to the influences of endpoints. In this letter, we uncover that synthesizing degree and H-index as the hybrid influences of endpoints can more reliably capture such endpoints with great and extensive maximum connected subgraph, which can more possibly attract other unlinked endpoints. In addition, the influence involving small heterogeneity of degree and H-index can further improve the accuracy of link prediction. Based on the hybrid influences of endpoints, we propose link prediction methods to explore the mechanism of link evolution. Extensive experiments on twelve real datasets suggest that the proposed methods can remarkably promote accuracy of link prediction.