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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.
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Frédéric Y. Bois, Ghislaine Gayraud. Probabilistic generation of random networks taking into account information on motifs occurrence. Journal of Computational Biology, Mary Ann Liebert, 2015, 22 (1), pp.25-36. ⟨10.1089/cmb.2014.0175⟩. ⟨ineris-01862569⟩

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