BIGDATA: Collaborative Research: F: Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics

许多具有社会重要性的现象，如疾病的传播和传染过程，可以用网络上的随机过程来模拟。这种网络现象的分析和控制本质上涉及基本的图论问题。遇到的图表通常是大规模的(具有数千万个节点)；此外，典型的实验分析涉及具有许多参数的大型设计，导致数十万次图表计算。需要新的方法来解决这些问题，因为快速响应时间是有效决策的关键。该项目的总体目标是为计算流行病学和传染病动力学中出现的图论问题开发有效的分布式算法和相关的下界。这将通过支持复杂分析的更有效的算法工具，对这些特定的应用程序产生重大影响。该项目还将对大规模网络中图问题的分布式算法的设计和分析做出基本贡献，并将产生一个具有执行大规模分布式图计算的构建块的算法工具包。该项目将为本科生和研究生以及公共卫生分析员带来重大的课程开发。最后，该项目将有助于让少数族裔和代表性不足的学生参与研究。该项目的技术重点将是针对图算法中的基本主题的分布式算法，例如图的连通性、距离、子图分析和不同类型的中心性度量。这些主题是不同类型传染过程的建模、模拟、分析和控制中反复出现的一些问题的基础。对于所有这些问题，该项目将专注于开发可证明有效的分布式算法，并在消息传递分布式计算模型下展示下界。PI还将开发这些算法的有效实现，并在流行病学中产生的真实世界图表中评估它们的性能和解决方案质量。在这些应用中出现的图形具有几个新的特征，这将为分布式计算带来新的挑战和机遇。

A number of phenomena of societal importance, such as the spread of diseases andcontagion processes, can be modeled by stochastic processes on networks. The analysis and control of such network phenomena involve, at their heart, fundamental graph-theoretic problems. The graphs encountered are typically of large-scale (having tens of millions of nodes); further, typical experimental analyses involve large designs with a number of parameters, leading to hundreds of thousands of graph computations. Novel methods for solving these problemsare needed, since fast response times are critical to effective decision making.The overarching goal of this project is to develop efficient distributed algorithms and associated lower bounds for graph-theoretic problems that arise in computational epidemiology and contagion dynamics. This will have a significant impact on these specific applications, through more efficient algorithmic tools for enabling complex analyses. The project will also make fundamental contributions to the design and analysis of distributed algorithms for graph problems in large-scale networks, and willresult in an algorithmic toolkit with building blocks for performing large-scale distributed graph computation. The project will lead to significant curriculum development for undergraduate as well as graduate students, as well as public health analysts. Finally, the project will help in involving minority and underrepresented students in research. The technical focus of the project will be on distributed algorithms for fundamental topics in graph algorithms such as graph connectivity, distances, subgraph analysis, and differentkinds of centrality measures. These topics underlie some of the recurring problems in the modeling, simulation and analysis and control of different kinds of contagion processes. For all these problems, the project will focus on developing provably efficient distributed algorithms and showing lower bounds under a message-passing distributed computing model. The PIs will also develop efficient implementations of these algorithms, and evaluate their performance and solution quality in real-world graphs arising in epidemiology. The graphs that arise in these applications have several novel characteristics, which will present new challenges as well as opportunities for distributed computing.