  Graph sampling allows mining a small representative subgraph from a big
graph. Sampling algorithms deploy different strategies to replicate the
properties of a given graph in the sampled graph. In this study, we provide a
comprehensive empirical characterization of five graph sampling algorithms on
six properties of a graph including degree, clustering coefficient, path
length, global clustering coefficient, assortativity, and modularity. We
extract samples from fifteen graphs grouped into five categories including
collaboration, social, citation, technological, and synthetic graphs. We
provide both qualitative and quantitative results. We find that there is no
single method that extracts true samples from a given graph with respect to the
properties tested in this work. Our results show that the sampling algorithm
that aggressively explores the neighborhood of a sampled node performs better
than the others.
