论文标题
niemagraphgen：一种记忆效率的全球尺度联系网络仿真工具包
NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit
论文作者
论文摘要
流行病模拟需要从各种随机图模型中采样接触网络的能力。现有方法可以模拟城市规模甚至国家规模的接触网络，但由于高度的记忆消耗，它们无法可行地模拟全球规模的联系网络。 Niemagraphgen（NGG）是一种记忆效率的图形生成工具，可实现全局尺度触点网络的模拟。 NGG避免将整个图存储在内存中，而是打算用于数据流管线中，从而导致内存消耗的数量级比现有工具小。 NGG为模拟社会接触网络提供了大规模的可估算解决方案，从而实现了全球尺度的流行仿真研究。
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.
