Link prediction based on local information considering preferential attachment

Shan Zeng

Physica A: Statistical Mechanics and its Applications, 2016, vol. 443, issue C, 537-542

Abstract: Link prediction in complex networks has attracted much attention in many fields. In this paper, a common neighbors plus preferential attachment index is presented to estimate the likelihood of the existence of a link between two nodes based on local information of the nearest neighbors. Numerical experiments on six real networks demonstrated the high effectiveness and efficiency of the new index compared with five well-known and widely accepted indices: the common neighbors, resource allocation index, preferential attachment index, local path index and Katz index. The new index provides competitively accurate prediction with local path index and Katz index while has less computational complexity and is more accurate than the other two indices.

Keywords: Link prediction; Complex networks; Similarity index; Node similarity (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:443:y:2016:i:c:p:537-542

DOI: 10.1016/j.physa.2015.10.016

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