Shortest path discovery of complex networks

Abstract

In this Rapid Communication we present an analytic study of sampled networks in the case of some important shortest-path sampling models. We present analytic formulas for the probability of edge discovery in the case of an evolving and a static network model. We also show that the number of discovered edges in a finite network scales much more slowly than predicted by earlier mean-field models. Finally, we calculate the degree distribution of sampled networks and we demonstrate that they are analogous to a destroyed network obtained by randomly removing edges from the original network.