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The problem of mapping human close-range proximity networks has been tackledusing a variety of technical approaches. Wearable electronic devices, inparticular, have proven to be particularly successful in a variety of settingsrelevant for research in social science, complex networks and infectiousdiseases dynamics. Each device and technology used for proximity sensing (e.g.,RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes withspecific biases on the close-range relations it records. Hence it is importantto assess which statistical features of the empirical proximity networks arerobust across different measurement techniques, and which modeling frameworksgeneralize well across empirical data. Here we compare time-resolved proximitynetworks recorded in different experimental settings and show that someimportant statistical features are robust across all settings considered. Theobserved universality calls for a simplified modeling approach. We show thatone such simple model is indeed able to reproduce the main statisticaldistributions characterizing the empirical temporal networks.

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