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Link prediction based on heterogeneous degree penalization with extending neighbors and clustering coefficient

Author

Listed:
  • Rongrong Song

    (School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China)

  • Guang Ling

    (School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China)

  • Qingju Fan

    (School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China)

  • Ming-Feng Ge

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, P. R. China)

  • Fang Wang

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, P. R. China)

Abstract

Link prediction, aiming to find missing links in a current network or to predict some possible new links in a future network, is a challenging problem in complex networks. Many existing link prediction algorithms perform the task by optimizing the node similarity measures, and then determining the possibility of the link between any pair of similar nodes. In this paper, we propose a novel node similarity index named heterogeneous degree penalization (HDP), which incorporates the quasi-local structure information of extending neighborhood of each pair of nodes to be predicted and the clustering coefficient of their common neighbors. For specific networks with different statistical properties, we can achieve a good performance of link prediction through adjusting the penalty weights. The experiment results show that, comparing with the other existing approaches, the proposed method can remarkably improve the accuracy of link prediction.

Suggested Citation

  • Rongrong Song & Guang Ling & Qingju Fan & Ming-Feng Ge & Fang Wang, 2022. "Link prediction based on heterogeneous degree penalization with extending neighbors and clustering coefficient," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-21, March.
    • Handle: RePEc:wsi:ijmpcx:v:33:y:2022:i:03:n:s0129183122500334
    DOI: 10.1142/S0129183122500334
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