Link prediction based on weighted synthetical influence of degree and H-index on complex networks

Zhenbao Wang, Yuxin Wang, Jinming Ma, Wenya Li, Ning Chen and Xuzhen Zhu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 527, issue C

Abstract: With the development of network technology, link prediction based on topological similarity has made remarkable achievements. Among all findings, however, researchers lay more emphasis on the information of paths between unlinked nodes, but less on that of endpoints. After extensive research, we find that the endpoint which possesses large and extensive maximum connected subgraph is more likely to attract other endpoints. And we can also find that endpoint which has both big degree and H-index possesses large and extensive maximum connected subgraph. So, in this paper, we propose a model based on weighted synthetical endpoint influence of degree and H-index and make extensive experiments on twelve real benchmark datasets. The results show that weighted synthetical influence performs better in accurate link prediction.

Keywords: Link prediction; Complex networks; Degree; H-index; Weighted synthetical endpoint influence (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307150

DOI: 10.1016/j.physa.2019.121184

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