  The sensitivity of networks regarding the removal of vertices has been
studied extensively within the last 15 years. A common approach to measure this
sensitivity is (i) removing successively vertices by following a specific
removal strategy and (ii) comparing the original and the modified network using
a specific comparison method. In this paper we apply a wide range of removal
strategies and comparison methods in order to study the sensitivity of
medium-sized networks from real world and randomly generated networks. In the
first part of our study we observe that social networks and web graphs differ
in sensitivity. When removing vertices, social networks are robust, web graphs
are not. This effect is conclusive with the work of Boldi et al. who analyzed
very large networks. For similarly generated random graphs we find that the
sensitivity highly depends on the comparison method. The choice of the removal
strategy has surprisingly marginal impact on the sensitivity as long as we
consider removal strategies implied by common centrality measures. However, it
has a strong effect when removing the vertices in random order.
