Impact of sampling design in estimation of graph characteristics

Studying structural and functional characteristics of large scale graphs (or networks) has been a challenging task due to the related computational overhead. Hence, most studies consult to sampling to gather necessary information to estimate various features of these big networks. On the other hand, using a best effort approach to graph sampling within the constraints of an application domain may not always produce accurate estimates. In fact, the mismatch between the characteristics of interest and the utilized network sampling methodology may result in incorrect inferences about the studied characteristics of the underlying system. In this study we empirically investigate the sources of information loss in a sampling process; identify the fundamental factors that need to be carefully considered in a sampling design; and use several synthetic and real world graphs to elaborately demonstrate the mismatch between the sampling design and graph characteristics of interest.