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Tag-aware link prediction algorithm in complex networks

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  • Wang, Jun
  • Zhang, Qian-Ming
  • Zhou, Tao

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.

Suggested Citation

  • Wang, Jun & Zhang, Qian-Ming & Zhou, Tao, 2019. "Tag-aware link prediction algorithm in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 105-111.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:105-111
    DOI: 10.1016/j.physa.2019.02.028
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    References listed on IDEAS

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    Cited by:

    1. Ma, Jinlong & Kong, Lingkang & Li, Hui-Jia, 2023. "An effective edge-adding strategy for enhancing network traffic capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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