Constrained bootstrapping of groups of nodes
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, my research focuses on the assessment of quantitative features of specific subset of nodes in empirical networks. We developed 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.