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

TL;DR

NiemaGraphGen is a memory-efficient toolkit that enables the simulation of global-scale contact networks for epidemic modeling by streaming data rather than storing entire graphs in memory.

Contribution

It introduces a novel streaming-based graph generation method that significantly reduces memory usage for large-scale contact network simulations.

Findings

Enables simulation of city- to global-scale contact networks

Reduces memory consumption by orders of magnitude

Facilitates large-scale epidemic studies

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.