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Journal articles
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.
Cited literature [44 references]
https://hal-ineris.archives-ouvertes.fr/ineris-01862569
Contributor : Gestionnaire Civs <>
Submitted on : Monday, August 27, 2018 - 2:21:58 PM
Last modification on : Tuesday, November 10, 2020 - 9:28:11 AM
Long-term archiving on: : Wednesday, November 28, 2018 - 4:38:40 PM
Contributor : Gestionnaire Civs <>
Submitted on : Monday, August 27, 2018 - 2:21:58 PM
Last modification on : Tuesday, November 10, 2020 - 9:28:11 AM
Long-term archiving on: : Wednesday, November 28, 2018 - 4:38:40 PM
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2015-353_post-print.pdf
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