Link prediction in complex networks based on the interactions among paths

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

Physica A: Statistical Mechanics and its Applications, 2018, vol. 510, issue C, 52-67

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.

Keywords: Complex networks; H-index; Link prediction; Resource receiving process (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:510:y:2018:i:c:p:52-67

DOI: 10.1016/j.physa.2018.06.051

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