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.
Lee et al. REF analyzed scale-free networks and three empirical networks suggesting that network properties such as betweenness centrality or clustering are sensitive to the choice of sampling method.
Statistical properties of sampled networks.
Phys. Rev. E 73, 016102 (2006)
Mathematics Physics Medicine