The control and risk assessment in complex information systems require to take
into account extremes arising from nodes with large node degrees. Various sampling techniques
like a Page Rank random walk, a Metropolis-Hastings Markov chain and others serve to collect
information about the nodes. The paper contributes to the comparison of sampling techniques in
complex networks by means of the first hitting time, that is the minimal time required to reach a
large node. Both the mean and distribution of the first hitting time is shown to be determined
by the so called extremal index. The latter indicates a dependence measure of extremes and also
reflects the cluster structure of the network. The clustering is caused by dependence between
nodes and heavy-tailed distributions of their degrees. Based on extreme value theory we estimate
the mean and the distribution of the first hitting time and the distribution of node degrees by
real data from social networks. We demonstrate the heaviness of the tails of these data using
appropriate tools. The same methodology can be applied to other complex networks like peerto-
peer telecommunication systems.
