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The European Physical Journal Special Topics

, Volume 222, Issue 6, pp 1295–1309 | Cite as

Empirical temporal networks of face-to-face human interactions

  • A. Barrat
  • C. Cattuto
  • V. Colizza
  • F. Gesualdo
  • L. Isella
  • E. Pandolfi
  • J. -F. Pinton
  • L. Ravà
  • C. Rizzo
  • M. Romano
  • J. Stehlé
  • A. E. Tozzi
  • W. Van den Broeck
Regular Article The Dynamics OF Networks: General Theory

Abstract

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented level of details and scale. Wearable sensors, in particular, open up a new window on human mobility and proximity in a variety of indoor environments. Here we review stylized facts on the structural and dynamical properties of empirical networks of human face-to-face proximity, measured in three different real-world contexts: an academic conference, a hospital ward, and a museum exhibition. First, we discuss the structure of the aggregated contact networks, that project out the detailed ordering of contact events while preserving temporal heterogeneities in their weights. We show that the structural properties of aggregated networks highlight important differences and unexpected similarities across contexts, and discuss the additional complexity that arises from attributes that are typically associated with nodes in real-world interaction networks, such as role classes in hospitals. We then consider the empirical data at the finest level of detail, i.e., we consider time-dependent networks of face-to-face proximity between individuals. To gain insights on the effects that causal constraints have on spreading processes, we simulate the dynamics of a simple susceptible-infected model over the empirical time-resolved contact data. We show that the spreading pathways for the epidemic process are strongly affected by the temporal structure of the network data, and that the mere knowledge of static aggregated networks leads to erroneous conclusions about the transmission paths on the corresponding dynamical networks.

Keywords

European Physical Journal Special Topic Dynamical Network Spreading Process Contact Network Temporal Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  • A. Barrat
    • 1
    • 2
  • C. Cattuto
    • 2
  • V. Colizza
    • 3
    • 4
    • 5
  • F. Gesualdo
    • 6
  • L. Isella
    • 2
  • E. Pandolfi
    • 6
  • J. -F. Pinton
    • 7
  • L. Ravà
    • 6
  • C. Rizzo
    • 8
  • M. Romano
    • 6
  • J. Stehlé
    • 1
    • 9
  • A. E. Tozzi
    • 6
  • W. Van den Broeck
    • 2
  1. 1.Centre de Physique ThéoriqueAix-Marseille Univ., CNRS UMR 6207, Univ. Sud Toulon VarMarseille Cedex 9France
  2. 2.Data Science LaboratoryISI FoundationTorinoItaly
  3. 3.INSERMParisFrance
  4. 4.Faculté de Médecine Pierre et Marie Curie, UMR S 707UPMC Université Paris 06ParisFrance
  5. 5.Computational Epidemiology LaboratoryISI FoundationTorinoItaly
  6. 6.Epidemiology UnitBambino Gesú HospitalRomeItaly
  7. 7.Laboratoire de Physique de l’École Normale Supérieure de Lyon, CNRS UMR 5672LyonFrance
  8. 8.Istituto Superiore di Sanità RomeNational Centre for Epidemiology, Surveillance and Health PromotionRomeItaly
  9. 9.Centre de Recherche en Economie et StatistiqueENSAEMalakoffFrance

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