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Coverage centralities for temporal networks

Author

Listed:
  • Taro Takaguchi

    (National Institute of Informatics
    JST, ERATO, Kawarabayashi Large Graph Project)

  • Yosuke Yano

    (JST, ERATO, Kawarabayashi Large Graph Project
    The University of Tokyo)

  • Yuichi Yoshida

    (National Institute of Informatics
    Preferred Infrastructure)

Abstract

Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of real-world temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:eurphb:v:89:y:2016:i:2:d:10.1140_epjb_e2016-60498-7
    DOI: 10.1140/epjb/e2016-60498-7
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    Cited by:

    1. 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).
    2. Ayana Aspembitova & Ling Feng & Valentin Melnikov & Lock Yue Chew, 2019. "Fitness preferential attachment as a driving mechanism in bitcoin transaction network," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    3. Andrew Mellor, 2019. "Event Graphs: Advances And Applications Of Second-Order Time-Unfolded Temporal Network Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-26, May.

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