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A generalized gravity model for influential spreaders identification in complex networks

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  • Li, Hanwen
  • Shang, Qiuyan
  • Deng, Yong

Abstract

How to identify influential spreaders in complex networks is still an open issue in network science. Many approaches from different perspectives have been proposed to identify vital nodes in complex networks. In these models, gravity model is an effective model to find vital nodes based on local information and path information. However, gravity model just uses degree of the node to judge local information, which is not precise. To address this issue, a generalized gravity model is proposed in this paper. Generalized gravity model measures local information from both local clustering coefficient and degree of each node, which is more comprehensive. Also, parameter α can be modified in different applications to get better performance. Generalized gravity model can degenerate into gravity model when α=0. Promising results from experiments on four real-world networks demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s0960077920308481
    DOI: 10.1016/j.chaos.2020.110456
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    7. Lei, Mingli & Cheong, Kang Hao, 2022. "Node influence ranking in complex networks: A local structure entropy approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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