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Identifying important nodes for temporal networks based on the ASAM model

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  • Jiang, Jiu-Lei
  • Fang, Hui
  • Li, Sheng-Qing
  • Li, Wei-Min

Abstract

The identification of important nodes in a temporal network is of great significance for the analysis and control of the information dissemination process. In this work, the multi-layer coupled network analysis method is employed to identify important nodes in a temporal network. First, to overcome the problem of a fixed constant being unable to reflect differences in the inter-layer coupling relationship, and by combining a node’s own neighbors and common neighbors of nodes in two-time layers, a new Enhanced Similarity Index (ESI) is proposed to measure the inter-layer coupling relationship. Secondly, the attenuation factor is introduced to more accurately describe the inter-layer coupling relationship. Finally, an Attenuation-Based Supra-Adjacency Matrix (ASAM) temporal network modeling method based on the attenuation of the inter-layer coupling strength is proposed. The importance of nodes in the temporal network is evaluated by calculating the eigenvector centrality of the nodes in each time layer in the temporal network. It is found that after deleting a certain percentage of the important nodes identified by the ASAM method, the temporal Largest Connected Component (LCC) of the network becomes smaller, and the network performance is improved as compared with the SAM and SSAM methods. The results indicate that the important nodes identified by the ASAM are at the core of the network and have a greater impact on the network structure and functions. This demonstrates that the proposed ASAM model can more effectively identify important nodes in the temporal network, and has significant application value in this research field.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007287
    DOI: 10.1016/j.physa.2021.126455
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    References listed on IDEAS

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    1. Zeng, Shan, 2016. "Link prediction based on local information considering preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 537-542.
    2. Taro Takaguchi & Yosuke Yano & Yuichi Yoshida, 2016. "Coverage centralities for temporal networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-11, February.
    3. 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.
    4. Yin, Ran-Ran & Guo, Qiang & Yang, Jian-Nan & Liu, Jian-Guo, 2018. "Inter-layer similarity-based eigenvector centrality measures for temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 165-173.
    5. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    6. Yang, Xu-Hua & Xiong, Zhen & Ma, Fangnan & Chen, Xiaoze & Ruan, Zhongyuan & Jiang, Peng & Xu, Xinli, 2021. "Identifying influential spreaders in complex networks based on network embedding and node local centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    7. Taro Takaguchi & Yosuke Yano & Yuichi Yoshida, 2016. "Coverage centralities for temporal networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-11, February.
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    Cited by:

    1. Hongyong Wang & Ping Xu & Fengwei Zhong, 2022. "Modeling and Feature Analysis of Air Traffic Complexity Propagation," Sustainability, MDPI, vol. 14(18), pages 1-21, September.

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