Change points, memory and epidemic spreading in temporal networks

Tiago P. Peixoto, Laetitia Gauvin

Research output: Contribution to journalArticle

Abstract

Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network. However, most data-driven approaches used to model time-varying networks attempt to capture only a single characteristic time scale in isolation — typically associated with the short-time memory of a Markov chain or with long-time abrupt changes caused by external or systemic events. Here we propose a unified approach to model both aspects simultaneously, detecting short and long-time behaviors of temporal networks. We do so by developing an arbitrary-order mixed Markov model with change points, and using a nonparametric Bayesian formulation that allows the Markov order and the position of change points to be determined from data without overfitting. In addition, we evaluate the quality of the multiscale model in its capacity to reproduce the spreading of epidemics on the temporal network, and we show that describing multiple time scales simultaneously has a synergistic effect, where statistically significant features are uncovered that otherwise would remain hidden by treating each time scale independently.

Original languageEnglish
Article number15511
JournalScientific Reports
Volume8
Issue number1
DOIs
Publication statusPublished - 19 Oct 2018

ASJC Scopus subject areas

  • General

Cite this

Change points, memory and epidemic spreading in temporal networks. / P. Peixoto, Tiago; Gauvin, Laetitia.

In: Scientific Reports, Vol. 8, No. 1, 15511, 19.10.2018.

Research output: Contribution to journalArticle

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