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The increasing availability of time --and space-- resolved data describinghuman activities and interactions gives insights into both static and dynamicproperties of human behavior. In practice, nevertheless, real-world datasetscan often be considered as only one realisation of a particular event. Thishighlights a key issue in social network analysis: the statistical significanceof estimated properties. In this context, we focus here on the assessment ofquantitative features of specific subset of nodes in empirical networks. Wepresent a method of statistical resampling based on bootstrapping groups ofnodes under constraints within the empirical network. The method enables us todefine acceptance intervals for various Null Hypotheses concerning relevantproperties of the subset of nodes under consideration, in order to characterizeby a statistical test its behavior as ``normal'' or not. We apply this methodto a high resolution dataset describing the face-to-face proximity ofindividuals during two co-located scientific conferences. As a case study, weshow how to probe whether co-locating the two conferences succeeded in bringingtogether the two corresponding groups of scientists.

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