Multivariate Generating Functions for Information Spread on Multi-Type Random Graphs
Statistical Mechanics
2023-06-21 v2 Populations and Evolution
Abstract
We study the spread of information on multi-type directed random graphs. In such graphs the vertices are partitioned into distinct types (communities) that have different transmission rates between themselves and with other types. We construct multivariate generating functions and use multi-type branching processes to derive an equation for the size of the large out-components in multi-type random graphs with a general class of degree distributions. We use our methods to analyse the spread of epidemics and verify the results with population based simulations
Cite
@article{arxiv.2106.12057,
title = {Multivariate Generating Functions for Information Spread on Multi-Type Random Graphs},
author = {Yaron Oz and Ittai Rubinstein and Muli Safra},
journal= {arXiv preprint arXiv:2106.12057},
year = {2023}
}
Comments
27 pages, 4 figures