Link prediction based on path entropy

Zhongqi Xu, Cunlai Pu and Jian Yang

Physica A: Statistical Mechanics and its Applications, 2016, vol. 456, issue C, 294-301

Abstract: Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, first we study the information entropy or uncertainty of a path using the information theory. After that, we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream of link predictors.

Keywords: Link prediction; Complex networks; Information entropy (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:456:y:2016:i:c:p:294-301

DOI: 10.1016/j.physa.2016.03.091

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