A new similarity measure for link prediction based on local structures in social networks

Farshad Aghabozorgi and Mohammad Reza Khayyambashi

Physica A: Statistical Mechanics and its Applications, 2018, vol. 501, issue C, 12-23

Abstract: Link prediction is a fundamental problem in social network analysis. There exist a variety of techniques for link prediction which applies the similarity measures to estimate proximity of vertices in the network. Complex networks like social networks contain structural units named network motifs. In this study, a newly developed similarity measure is proposed where these structural units are applied as the source of similarity estimation. This similarity measure is tested through a supervised learning experiment framework, where other similarity measures are compared with this similarity measure. The classification model trained with this similarity measure outperforms others of its kind.

Keywords: Network motifs; Link prediction; Node similarity; Supervised learning; Social networks (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:501:y:2018:i:c:p:12-23

DOI: 10.1016/j.physa.2018.02.010

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