  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.
