Generating conditional uniform random networks by optimization procedures

Complex networks is a recent area of research motivated by the empirical study of realworld networks, such as social relations, protein interaction, neuronal connections, etc. As closed-form probabilistic models of networks are often not available, the ability of randomly generating networks verifying specific constraints might be useful. The purpose of this work is to develop optimization-based procedures to randomly generate networks with structural constraints, within the probabilistic framework of conditional uniform models. Based on the characterization of families of networks by means of systems of linear constraints, polynomialtime methods to generate networks with specified structural properties are constructed.