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.

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Cited by in corpus (6)

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• Latent geometry and dynamics of proximity networks
• Estimating household contact matrices structure from easily collectable metadata
• Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm
• Hyperbolic Mapping of Human Proximity Networks