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Predicting links based on knowledge dissemination in complex network

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  • Zhou, Wen
  • Jia, Yifan

Abstract

Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method–knowledge dissemination based link prediction (KDLP)–is proposed to test KD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks.

Suggested Citation

  • Zhou, Wen & Jia, Yifan, 2017. "Predicting links based on knowledge dissemination in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 561-568.
  • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:561-568
    DOI: 10.1016/j.physa.2016.12.067
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    Cited by:

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    5. Wei Chen & Hui Qu & Kuo Chi, 2021. "Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches," Sustainability, MDPI, vol. 13(2), pages 1-16, January.

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