A scale-free graph model based on bipartite graphs
Most biological networks have some common properties, on which models have to fit. The main one is that those networks are scale-free, that is that the distribution of the vertex degrees follows a power-law. Among the existing models, the ones which fit those characteristics best are based on a time evolution which makes impossible the analytic calculation of the number of motifs in the network. Focusing on applications, this calculation is very important to decompose networks in a modular manner, as proposed by Milo etal.. On the contrary, models whose construction does not depend on time, miss one or several properties of real networks or are not computationally tractable. In this paper, we propose a new random graph model that satisfies the global features of biological networks and the non-time-dependency condition. It is based on a bipartite graph structure, which has a biological interpretation in metabolic networks.
scale-free bipartite graph random graph complex network