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arXiv · 1311.6443

Probabilistic generation of random networks taking into account information on motifs occurrence

Frederic Y. Bois·Ghislaine Gayraud
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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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Frederic Y. Bois, Ghislaine Gayraud. 2013-11-25. Probabilistic generation of random networks taking into account information on motifs occurrence. https://doi.org/10.1089/cmb.2014.0175

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