NDLI: Self-Adaptive Optimized Link Prediction Based on Weak Ties Theory in Unweighted Network

Content Provider	IEEE Xplore Digital Library
Author	Xuzhen Zhu Hui Tian Zheng Hu Haifeng Liu
Copyright Year	2013
Description	Author affiliation: State Key Lab. of Networking & Switching Technol., Beijing Univ. of Posts & Telecommun., Beijing, China (Xuzhen Zhu; Hui Tian; Zheng Hu; Haifeng Liu)
Abstract	Recently, link prediction becomes the focus of researchers from various fields of science, and considerable achievements have been obtained. Algorithms based on similarities constitute the research mainstream. This paper proposes an optimization to the classical algorithms: Adamic-Adar (AA) and Resource Allocation (RA) with the same foundation in local similarity. Herein, weak ties theory will be introduced to improve accuracy of AA and RA. Although Lü et al. discussed the functions of weak ties in AA and RA, only weighted network was considered. Then this paper will pay more attention to unweighted and undirected simple network. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in five real networks to compare classical algorithms with the optimized index in the network through numerical analysis.
Starting Page	896
Ending Page	900
File Size	313301
Page Count	5
File Format	PDF
ISBN	9780769551371
DOI	10.1109/SocialCom.2013.137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Link prediction Accuracy Social network services Complex network Predictive models Self-adaptive Prediction algorithms Indexes Optimization Weak ties
Content Type	Text
Resource Type	Article

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