  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.
