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.

• Publication year

  2013

• Venue

  J. Comput. Biol.

• Publication date

  2013-11-25

• Fields of study

  Biology, Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.1089/cmb.2014.0175arXiv 1311.6443PMID 25493547PMCID PMC4283061
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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