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

TL;DR

This paper compares proximity networks recorded with different sensing techniques, identifies robust statistical features, and proposes a simplified model that captures key properties across diverse empirical data.

Contribution

It demonstrates the universality of certain statistical features in proximity networks and introduces a simple modeling framework that generalizes across different measurement methods.

Findings

Robust statistical features are consistent across various proximity sensing techniques.

A simplified model can reproduce main statistical distributions of empirical proximity networks.

Universality suggests potential for standardized modeling approaches.

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.