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Lately, network sampling proved as a promising tool for simplifying largereal-world networks and thus providing for their faster and more efficientanalysis. Still, understanding the changes of network structure and propertiesunder different sampling methods remains incomplete. In this paper, we analyzethe presence of characteristic group of nodes (i.e., communities, modules andmixtures of the two) in social and information networks. Moreover, we observethe changes of node group structure under two sampling methods, random nodeselection based on degree and breadth-first sampling. We show that the sampledinformation networks contain larger number of mixtures than original networks,while the structure of sampled social networks exhibits strongercharacterization by communities. The results also reveal there exist nosignificant differences in the behavior of both sampling methods. Accordingly,the selection of sampling method impact on the changes of node group structureto a much smaller extent that the type and the structure of analyzed network.

Sampling node group structure of social and information networks