Bootstrapping under constraint for the assessment of group behavior in human contact networks

TL;DR

This paper introduces a bootstrapping method for assessing the statistical significance of group behaviors in human contact networks, enabling robust hypothesis testing on empirical data.

Contribution

It presents a novel resampling approach under constraints for analyzing subset properties in contact networks, addressing the challenge of single realisation datasets.

Findings

Method successfully distinguishes between normal and abnormal group behaviors.

Application to conference data reveals insights into group integration.

Provides a framework for significance testing in social network analysis.

Abstract

The increasing availability of time --and space-- resolved data describing human activities and interactions gives insights into both static and dynamic properties of human behavior. In practice, nevertheless, real-world datasets can often be considered as only one realisation of a particular event. This highlights a key issue in social network analysis: the statistical significance of estimated properties. In this context, we focus here on the assessment of quantitative features of specific subset of nodes in empirical networks. We present a method of statistical resampling based on bootstrapping groups of nodes under constraints within the empirical network. The method enables us to define acceptance intervals for various Null Hypotheses concerning relevant properties of the subset of nodes under consideration, in order to characterize by a statistical test its behavior as ``normal'' or not. We apply this method to a high resolution dataset describing the face-to-face proximity of individuals during two co-located scientific conferences. As a case study, we show how to probe whether co-locating the two conferences succeeded in bringing together the two corresponding groups of scientists.