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Co-Ranking for nodes, layers and timestamps in multilayer temporal networks

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  • Zhang, Ting
  • Zhang, Kun
  • Lv, Laishui
  • Bardou, Dalal

Abstract

Understanding the structure of multilayer temporal networks requires the evaluation of nodes importance, the relationship between them and the timestamps simultaneously. In this paper,we propose a parameters-free centrality algorithm referred to as Co-Rank. The proposed algorithm uses a sixth-order tensor to describe the multilayer temporal network which considers the inter-layer connections between the adjacent timestamps across different layers. After describing the multilayer temporal network, the next step is to build and solve a set of tensor equations following the mutual relationships to get the centrality. The existence of the centrality metric is formally proven, and the convergence of the Co-Rank is also shown so that it can be effectively applied for the ranking. The results of experiments on synthetic and real-world networks show the effectiveness of our proposed algorithm.

Suggested Citation

  • Zhang, Ting & Zhang, Kun & Lv, Laishui & Bardou, Dalal, 2019. "Co-Ranking for nodes, layers and timestamps in multilayer temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 88-96.
    • Handle: RePEc:eee:chsofr:v:125:y:2019:i:c:p:88-96
    DOI: 10.1016/j.chaos.2019.05.021
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    3. Pietro Panzarasa & Tore Opsahl & Kathleen M. Carley, 2009. "Patterns and dynamics of users' behavior and interaction: Network analysis of an online community," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(5), pages 911-932, May.
    4. Manlio De Domenico & Albert Solé-Ribalta & Elisa Omodei & Sergio Gómez & Alex Arenas, 2015. "Ranking in interconnected multilayer networks reveals versatile nodes," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
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    Cited by:

    1. 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).
    2. Jiang, Jiu-Lei & Fang, Hui & Li, Sheng-Qing & Li, Wei-Min, 2022. "Identifying important nodes for temporal networks based on the ASAM model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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