title: Probabilistic generation of random networks taking into account information on motifs occurrence.
abstract: Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.
authors.affiliation: 1 Université de Technologie de Compiègne and Institut National de l'Environnement Industriel et des Risques, France .
authors.forename: Frederic Y
authors.initials: FY
authors.lastname: Bois
authors.forename: Ghislaine
authors.initials: G
authors.lastname: Gayraud
mesh_terms: D004926:Escherichia coli / Q000502:physiology*; D053263:Gene Regulatory Networks / Q000502:physiology*; D008957:Models, Genetic; D014158:Transcription, Genetic / Q000502:physiology*
keywords: biological network; graphical model; network motif; prior information