Tag-aware link prediction algorithm in complex networks

Jun Wang, Qian-Ming Zhang and Tao Zhou

Physica A: Statistical Mechanics and its Applications, 2019, vol. 523, issue C, 105-111

Abstract: Revealing the mechanisms driving network evolution has long been a challenge in the field of complex networks. An effective research technique is to predict missing links following some certain mechanisms. If the principle of a predictor is consistent to the mechanism of a given network, this predictor should provide more accurate predictions. Up to present, the majority of related researches only considered structural features. However, tag information usually plays an important role when a node create links to other nodes. Some pioneers have considered the similarity between nodes’ tags, but we claim that a node’s tags are insufficient to profile this node, and the tags of this node’s neighbors are also considerable. In this paper, given a node vi, we introduce entropy to measure the homogeneity of the tag system consisting of vi’s tags and vi’s neighbors’ tags, and then re-estimate the influence of vi to other nodes. Finally, we propose a link prediction algorithm by harnessing the homogeneity of the tag system and get more accurate predictions.

Keywords: Complex network; Link prediction; Entropy; Tag; Attractivity (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:523:y:2019:i:c:p:105-111

DOI: 10.1016/j.physa.2019.02.028

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