  We study the statistical properties of the sampled scale-free networks,
deeply related to the proper identification of various real-world networks. We
exploit three methods of sampling and investigate the topological properties
such as degree and betweenness centrality distribution, average path length,
assortativity, and clustering coefficient of sampled networks compared with
those of original networks. It is found that the quantities related to those
properties in sampled networks appear to be estimated quite differently for
each sampling method. We explain why such a biased estimation of quantities
would emerge from the sampling procedure and give appropriate criteria for each
sampling method to prevent the quantities from being overestimated or
underestimated.
