Probabilistic generation of random networks taking into account information on motifs occurrence
Quantitative Methods
2017-05-03 v1 Molecular Networks
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.
Keywords
Cite
@article{arxiv.1311.6443,
title = {Probabilistic generation of random networks taking into account information on motifs occurrence},
author = {Frederic Y. Bois and Ghislaine Gayraud},
journal= {arXiv preprint arXiv:1311.6443},
year = {2017}
}