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The shape of memory in temporal networks

Author

Listed:
  • Oliver E. Williams

    (School of Mathematical Sciences, Queen Mary University of London)

  • Lucas Lacasa

    (Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC))

  • Ana P. Millán

    (Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience
    Institute Carlos I for Theoretical and Computational Physics, University of Granada)

  • Vito Latora

    (School of Mathematical Sciences, Queen Mary University of London
    Dipartimento di Fisica ed Astronomia, Università di Catania and INFN
    Complexity Science Hub Vienna (CSHV))

Abstract

How to best define, detect and characterize network memory, i.e. the dependence of a network’s structure on its past, is currently a matter of debate. Here we show that the memory of a temporal network is inherently multidimensional, and we introduce a mathematical framework for defining and efficiently estimating the microscopic shape of memory, which characterises how the activity of each link intertwines with the activities of all other links. We validate our methodology on a range of synthetic models, and we then study the memory shape of real-world temporal networks spanning social, technological and biological systems, finding that these networks display heterogeneous memory shapes. In particular, online and offline social networks are markedly different, with the latter showing richer memory and memory scales. Our theory also elucidates the phenomenon of emergent virtual loops and provides a novel methodology for exploring the dynamically rich structure of complex systems.

Suggested Citation

  • Oliver E. Williams & Lucas Lacasa & Ana P. Millán & Vito Latora, 2022. "The shape of memory in temporal networks," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28123-z
    DOI: 10.1038/s41467-022-28123-z
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    References listed on IDEAS

    as
    1. Corsi, Fulvio & Lillo, Fabrizio & Pirino, Davide & Trapin, Luca, 2018. "Measuring the propagation of financial distress with Granger-causality tail risk networks," Journal of Financial Stability, Elsevier, vol. 38(C), pages 18-36.
    2. Mazzarisi, P. & Barucca, P. & Lillo, F. & Tantari, D., 2020. "A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market," European Journal of Operational Research, Elsevier, vol. 281(1), pages 50-65.
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    Cited by:

    1. Luca Gallo & Lucas Lacasa & Vito Latora & Federico Battiston, 2024. "Higher-order correlations reveal complex memory in temporal hypergraphs," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    2. Tian, Yang & Tian, Hui & Cui, Qimei & Zhu, Xuzhen, 2024. "Phase transition phenomena in social propagation with dynamic fashion tendency and individual contact," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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