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

Area of Science:

• Computational epidemiology
• Network science
• Data science

Background:

• Accurate epidemic simulations rely on realistic contact network models.
• Current graph generation methods struggle with the memory demands of global-scale networks.
• This limits the scope of large-scale epidemiological studies.

Purpose of the Study:

• To introduce NiemaGraphGen (NGG), a novel tool for memory-efficient generation of large-scale contact networks.
• To overcome the memory limitations of existing graph generation tools for global network simulation.
• To enable more comprehensive and scalable epidemic modeling.

Main Methods:

• NGG employs a memory-efficient approach, avoiding full graph storage in RAM.
• The tool is designed for data streaming pipelines, significantly reducing memory footprint.
• Utilizes principles of random graph models for network construction.

Main Results:

• NGG achieves memory consumption orders of magnitude lower than existing tools.
• Demonstrates the feasibility of simulating global-scale social contact networks.
• Provides a massively scalable solution for network generation.

Conclusions:

• NiemaGraphGen (NGG) effectively addresses memory constraints in large-scale network simulation.
• The tool facilitates global-scale epidemic studies, advancing computational epidemiology.
• NGG represents a significant advancement in simulating complex social contact structures.