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

Niema Moshiri
Published in: GigaByte (Hong Kong, China) (2022)
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.
Keyphrases
  • working memory
  • healthcare
  • machine learning
  • convolutional neural network
  • deep learning
  • artificial intelligence