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Memory in network flows and its effects on spreading dynamics and community detection

Author

Listed:
  • Martin Rosvall

    (Integrated Science Lab, Umeå University)

  • Alcides V. Esquivel

    (Integrated Science Lab, Umeå University)

  • Andrea Lancichinetti

    (Integrated Science Lab, Umeå University
    Howard Hughes Medical Institute (HHMI), Northwestern University)

  • Jevin D. West

    (Integrated Science Lab, Umeå University
    Information School, University of Washington)

  • Renaud Lambiotte

    (University of Namur)

Abstract

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and although we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.

Suggested Citation

  • Martin Rosvall & Alcides V. Esquivel & Andrea Lancichinetti & Jevin D. West & Renaud Lambiotte, 2014. "Memory in network flows and its effects on spreading dynamics and community detection," Nature Communications, Nature, vol. 5(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5630
    DOI: 10.1038/ncomms5630
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