A critical point for random graphs with a given degree sequence
Random Graphs 93 Proceedings of the sixth international seminar on Random graphs and probabilistic methods in combinatorics and computer science
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Regular Article: The Diameter of Sparse Random Graphs
Advances in Applied Mathematics
The Size of the Giant Component of a Random Graph with a Given Degree Sequence
Combinatorics, Probability and Computing
Bipartite structure of all complex networks
Information Processing Letters
Reaction motifs in metabolic networks
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
| Hi-index | 0.04 |
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 et al.. 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.