Probabilistic generation of random networks taking into account information on motifs occurrence.
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.
['Bois|Frederic Y|FY|', 'Gayraud|Ghislaine|G|']
D004926:Escherichia coli D053263:Gene Regulatory Networks D008957:Models, Genetic D014158:Transcription, Genetic
biological network graphical model network motif prior information