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Link prediction in complex networks based on communication capacity and local paths

  • Regular Article - Statistical and Nonlinear Physics
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Abstract

Link prediction is one of the most essential and significant research issues in network science, which aims at finding unknown, missing or future links in complex networks. Numerous similarity-based algorithms have been widely applied due to low computational cost and high prediction accuracy. To achieve a good balance between prediction accuracy and computational complexity, we design a novel link prediction algorithm to predict potential links based on Communication Capabilities and Local Paths (CCLP). The core idea of the proposed algorithm is that the similarity of node pairs is closely bound up with the amount of resources transmitted between them. In this algorithm, we introduce communicability network matrix to calculate the resource transmission capability of nodes, and then integrate it with local paths to measure the amount of resources transferred between nodes. We conduct several groups of comparative experiments on 12 real-world networks to validate the effectiveness of the proposed algorithm. Experimental results demonstrate that CCLP has achieved better performance than four classical similarity indices and five popular algorithms.

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Data availability statement

This manuscript has associated data in a data repository. [Authors’ comment: The datasets utilized in this paper are public and can be downloaded from the following web sites. https://www-personal.umich.edu/mejn/netdata/ and https://vlado.fmf.uni-lj.si/pub/networks/pajek/data/gphs.htm.

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Acknowledgements

The authors would like to sincerely and deeply thank the editor and the anonymous referees for their helpful comments and constructive suggestions. This work was supported by grants from the National Natural Science Foundation of China (Project No. 11871328) and the Shanghai Science and Technology Development Funds Soft Science Research Project (Grant No. 21692109800).

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Authors and Affiliations

  1. Department of Information Management, School of Management, Shanghai University, Shanghai, 200444, China

    Jing Peng, Guiqiong Xu, Xiaoyu Zhou, Chen Dong & Lei Meng

Authors
  1. Jing Peng
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  2. Guiqiong Xu
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  3. Xiaoyu Zhou
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  4. Chen Dong
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  5. Lei Meng
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Contributions

JP and GX developed the theory and methodology; JP and XZ conducted simulated experiments and validation; CD wrote part of original draft; LM reviewed the manuscript; All authors discussed the results and collaborated in writing the manuscript.

Corresponding author

Correspondence to Guiqiong Xu.

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Peng, J., Xu, G., Zhou, X. et al. Link prediction in complex networks based on communication capacity and local paths. Eur. Phys. J. B 95, 152 (2022). https://doi.org/10.1140/epjb/s10051-022-00415-9

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