Volume 122, Number 6, June 2018
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||06 August 2018|
Hybrid influence of degree and H-index in the link prediction of complex networks
1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Beijing, 100876, China
2 School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu, 611731, China
3 Big Data Research Center, University of Electronic Science and Technology of China - Chengdu, 611731, China
Received: 23 May 2018
Accepted: 10 July 2018
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.
PACS: 89.65.-s – Social and economic systems / 89.75.Hc – Networks and genealogical trees / 89.75.-k – Complex systems
© EPLA, 2018
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