Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a data streaming pipeline, resulting in memory consumption that is orders of magnitude smaller than existing tools. NGG provides a massively-scalable solution for simulating social contact networks, enabling global-scale epidemic simulation studies.
Cite
@article{arxiv.2201.04625,
title = {NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit},
author = {Niema Moshiri},
journal= {arXiv preprint arXiv:2201.04625},
year = {2022}
}