Correlated random networks

We develop a statistical theory of networks. A network is a set of vertices and links given by its adjacency matrix \c, and the relevant statistical ensembles are defined in terms of a partition function Z=\sum_{\c} \exp {[}-\beta \H(\c) {]}. The simplest cases are uncorrelated random networks such as the well-known Erd\"os-R\'eny graphs. Here we study more general interactions $\H(\c)$ which lead to {\em correlations}, for example, between the connectivities of adjacent vertices. In particular, such correlations occur in {\em optimized} networks described by partition functions in the limit $\beta \to \infty$. They are argued to be a crucial signature of evolutionary design in biological networks.