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NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit

2022-01-13 17:13:49
Niema Moshiri
arXiv_CV
 
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2201.04625

PDF

https://arxiv.org/pdf/2201.04625.pdf

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