Link prediction based on the tie connection strength of common neighbor

Yujie Yang, Jianhua Zhang, Xuzhen Zhu, Jinming Ma () and Xin Su ()
Additional contact information
Yujie Yang: Institute of Computer and Information Technology, Henan Normal University, Xinxiang 453007, P. R. China†State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Jianhua Zhang: #x2020;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Xuzhen Zhu: #x2020;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Jinming Ma: #x2020;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Xin Su: #x2021;School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 11, 1-16

Abstract: Traditional link prediction indices focus on the degree of the common neighbor and consider that the common neighbor with large degree contributes less to the similarity of two unconnected endpoints. Therefore, some of the local information-based methods only restrain the common neighbor with large degree for avoiding the influence dissipation. We find, however, if the large degree common neighbor connects with two unconnected endpoints through multiple paths simultaneously, these paths actually serve as transmission influences instead of dissipation. We regard these paths as the tie connection strength (TCS) of the common neighbor, and larger TCS can promote two unconnected endpoints to link with each other. Meanwhile, we notice that the similarity of node-pairs also relates to the network topology structure. Thus, in order to study the influences of TCS and the network structure on similarity, we introduce a free parameter and propose a novel link prediction method based on the TCS of the common neighbor. The experiment results on 12 real networks suggest that the proposed TCS index can improve the accuracy of link prediction.

Keywords: Link prediction; tie connection strength; common neighbor; complex networks (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S012918311950089X
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:30:y:2019:i:11:n:s012918311950089x

Ordering information: This journal article can be ordered from

DOI: 10.1142/S012918311950089X

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().