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The structured backbone of temporal social ties

Author

Listed:
  • Teruyoshi Kobayashi

    (Center for Computational Social Science, Kobe University)

  • Taro Takaguchi

    (Toda)

  • Alain Barrat

    (Aix Marseille Univ, Université de Toulon, CNRS, CPT
    Data Science Laboratory, ISI Foundation)

Abstract

In many data sets, information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections is a non-trivial task. Moreover, mehods put forward until now do not deal with time-resolved network data, which have become increasingly available. Here we develop a method for filtering temporal network data, by defining an adequate temporal null model that allows us to identify pairs of nodes having more interactions than expected given their activities: the significant ties. Moreover, our method can assign a significance to complex structures such as triads of simultaneous interactions, an impossible task for methods based on static representations. Our results hint at ways to represent temporal networks for use in data-driven models.

Suggested Citation

  • Teruyoshi Kobayashi & Taro Takaguchi & Alain Barrat, 2019. "The structured backbone of temporal social ties," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-018-08160-3
    DOI: 10.1038/s41467-018-08160-3
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    Cited by:

    1. Wu, Jiayun & He, Langzhou & Jia, Tao & Tao, Li, 2023. "Temporal link prediction based on node dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    2. Mattia Mazzoli & Riccardo Gallotti & Filippo Privitera & Pere Colet & José J. Ramasco, 2023. "Spatial immunization to abate disease spreading in transportation hubs," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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