网络人群传染动力学的互动探索和理解

S. Abdelhamid, C. Kuhlman, M. Marathe, S. Ravi
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引用次数: 7

摘要

网络人群传染过程的建模和模拟被用来理解抗议、社会动荡、信息传播、病毒和疾病流行以及其他现象。网络结构和顶点和边缘的属性通常有助于解释传染的传播过程。然而,特别是对于较大的网络(例如,具有数十万或数百万个顶点的网络),由于这些模拟的规模,对传染传播结果进行推理和理解是困难的。我们提出了一个名为NEMO的网络应用程序,用于协助分析师理解传染过程并建立因果关系。它有几个功能来查询和可视化网络、子网及其属性。除了解释NEMO的功能外,我们还提供了一个关于埃博拉病毒在非洲利比里亚400万个顶点的社交网络上传播的真实案例研究。我们演示了NEMO如何用于探索交互式网络,以了解不同干预措施有效性的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactive exploration and understanding of contagion dynamics in networked populations
Modeling and simulation of contagion processes on networked populations are used to understand protests, social unrest, the spread of information, and virus and disease epidemics, among other phenomena. Network structure and attributes of vertices and edges are often useful in explaining contagion spreading processes. However, particularly for larger networks (e.g., those with hundreds of thousands or millions of vertices), reasoning about and making sense of contagion propagation results is difficult owing to the scale of these simulations. We present a web application called NEMO for assisting an analyst in understanding contagion processes and in establishing causality. It has several features to query and visualize networks, subnetworks, and their properties. In addition to explaining NEMO's features, we provide a real case study of the spread of Ebola on a 4-million-vertex social network of Liberia, Africa. We demonstrate how NEMO can be used to explore interactively networks to understand the reasons for the effectiveness of different interventions.
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