  Lately, network sampling proved as a promising tool for simplifying large
real-world networks and thus providing for their faster and more efficient
analysis. Still, understanding the changes of network structure and properties
under different sampling methods remains incomplete. In this paper, we analyze
the presence of characteristic group of nodes (i.e., communities, modules and
mixtures of the two) in social and information networks. Moreover, we observe
the changes of node group structure under two sampling methods, random node
selection based on degree and breadth-first sampling. We show that the sampled
information networks contain larger number of mixtures than original networks,
while the structure of sampled social networks exhibits stronger
characterization by communities. The results also reveal there exist no
significant differences in the behavior of both sampling methods. Accordingly,
the selection of sampling method impact on the changes of node group structure
to a much smaller extent that the type and the structure of analyzed network.
