Uncovering missing links with cold ends

Yu-Xiao Zhu, Linyuan Lü, Qian-Ming Zhang and Tao Zhou

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 22, 5769-5778

Abstract: To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set. We argue that this straightforward and standard method may lead to terrible bias, since in real biological and information networks, missing links are more likely to be links connecting low-degree nodes. We therefore study how to uncover missing links with low-degree nodes, namely links in the probe set are of lower degree products than a random sampling. Experimental analysis on ten local similarity indices and four disparate real networks reveals a surprising result that the Leicht–Holme–Newman index [E.A. Leicht, P. Holme, M.E.J. Newman, Vertex similarity in networks, Phys. Rev. E 73 (2006) 026120] performs the best, although it was known to be one of the worst indices if the probe set is a random sampling of all links. We further propose an parameter-dependent index, which considerably improves the prediction accuracy. Finally, we show the relevance of the proposed index to three real sampling methods: acquaintance sampling, random-walk sampling and path-based sampling.

Keywords: Complex networks; Link prediction; Cold ends; Sampling methods (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:22:p:5769-5778

DOI: 10.1016/j.physa.2012.06.003

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