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Link prediction in complex networks based on the interactions among paths

Author

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  • Yao, Yabing
  • Zhang, Ruisheng
  • Yang, Fan
  • Tang, Jianxin
  • Yuan, Yongna
  • Hu, Rongjing

Abstract

Link prediction in incomplete complex networks is an important issue in network science. Recently, various structure-based similarity methods have been proposed. However, most path-dependent methods merely pay attention to the contributions of paths with specific length, which neglects the interactions of paths with different length for performance improvement. Motivated by the resource-traffic flow mechanism on networks, we measure the interaction relationship of paths with a resource receiving process. In this process, each node takes certain initial resources quantified by its H-index, and then the intermediate nodes on paths can receive resources from their neighbours. Based on this process, a local path-based link predictor which emphasizes the effect of the Resources from Short Paths (RSP) is proposed. Experiments on twelve real-world networks demonstrate that the RSP index has better performance than other nine structure-based similarity methods.

Suggested Citation

  • Yao, Yabing & Zhang, Ruisheng & Yang, Fan & Tang, Jianxin & Yuan, Yongna & Hu, Rongjing, 2018. "Link prediction in complex networks based on the interactions among paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 52-67.
    • Handle: RePEc:eee:phsmap:v:510:y:2018:i:c:p:52-67
    DOI: 10.1016/j.physa.2018.06.051
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    References listed on IDEAS

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