  Complex networks are now being studied in a wide range of disciplines across
science and technology. In this paper we propose a method by which one can
probe the properties of experimentally obtained network data. Rather than just
measuring properties of a network inferred from data, we aim to ask how typical
is that network? What properties of the observed network are typical of all
such scale free networks, and which are peculiar? To do this we propose a
series of methods that can be used to generate statistically likely complex
networks which are both similar to the observed data and also consistent with
an underlying null-hypothesis -- for example a particular degree distribution.
There is a direct analogy between the approach we propose here and the
surrogate data methods applied to nonlinear time series data.
