Robust modeling of human contact networks across different scales and proximity-sensing techniques

Abstract : The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks.
Type de document :
Communication dans un congrès
Socinfo, Sep 2017, Oxford, United Kingdom. 〈http://socinfo2017.oii.ox.ac.uk/〉
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https://hal.archives-ouvertes.fr/hal-01567309
Contributeur : Alain Barrat <>
Soumis le : samedi 22 juillet 2017 - 14:09:47
Dernière modification le : dimanche 23 juillet 2017 - 01:07:08

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  • HAL Id : hal-01567309, version 1
  • ARXIV : 1707.06632

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Michele Starnini, Bruno Lepri, Andrea Baronchelli, Alain Barrat, C. Cattuto, et al.. Robust modeling of human contact networks across different scales and proximity-sensing techniques . Socinfo, Sep 2017, Oxford, United Kingdom. 〈http://socinfo2017.oii.ox.ac.uk/〉. 〈hal-01567309〉

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