Physicists have suddenly found social networks. Triggered by the modelling of 'small world architecture' presented by Watts and Strogatz in Nature (June, 1998), physicists, computer scientists, and mathematicians have produce a flood of materials on social, ecological, biological and biochemical networks. They have generated working models of network dynamics and complex network simulations with many applications. Publicists in the field talk of a new, comprehensive 'science of networks' with the potential to supersede current social theories. In this paper I describe how social network analysts have, independently, developed simulation models of social networks. For the first time, these techniques allow us to generate the array of all possible networks of the size and density of an observed network. This allows empirical sociological researchers to identify features of an observed network that are unusual and, thus, make probabilistic assessments parallel to those of standard statistics. We illustrate this technique on an example network of interlocking company directorates in Australia. In conclusion we suggest how this methodology would develop for this example and reflect on the interests of sociology and complexity science in this area of work.