EAGER: SSDIM: Multiscale Methods for Generating Infrastructure Networks

EAGER：SSDIM：生成基础设施网络的多尺度方法

• 批准号: 1745300
• 负责人: Ilya Safro
• 金额: $ 20万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745300&HistoricalAwards=false
• 关键词: EAGER; SSDIM; Multiscale; Methods; Generating

The ultimate goal of this EArly-concept Grant for Exploratory Research (EAGER) project is to develop a crossdomain multiscale network and interdependent critical infrastructure (ICI) generator that captures many features of real networks and incorporates an arbitrarily large or small degree of stochasticity. Starting from samples of known or hypothesized networks, the generator will synthesize ensembles of networks and ICIs that will preserve, on average, a diverse set of topological and physical design properties at multiple scales of its structure. These properties will include several measures of centrality, assortativity, path lengths, clustering, flows, required physical capacities, and modularity. This approach introduces an unbiased variability across the ensemble in many of these properties at multiple scales which creates a desired realism of the synthesized system. The models will include human factor components incorporated in both topological and physical designs. A toolbox of algorithms and heuristics for generating synthetic networks of infrastructures and ICIs will be developed, and generated benchmarks will be disseminated. Outcomes of this work will facilitate such tasks as simulation, policy testing and decision making for ICIs enabled by fundamental advancement in network generation algorithms. The toolbox will be designed using a modular approach that will allow it to evolve and to be applied in other domains. The interdisciplinary team of investigators comprising expertise in computer science, behavioral science, civil engineering, and network science and public health will ideally support the goal of realistic synthetic data generation. Perspectives and methodological approaches from network analysis, big data systems, machine learning, water networks, and organizational sciences will be brought to bear to develop a toolkit of methods that include a combination of network analytics, optimization, and statistical analysis techniques. The resulting products will include developed algorithms and generated synthetic datasets disseminated for a broad scientific community.

这个早期概念探索性研究资助 (EAGER) 项目的最终目标是开发一个跨域多尺度网络和相互依赖的关键基础设施 (ICI) 生成器，该生成器捕获真实网络的许多特征并包含任意大小的随机性。从已知或假设的网络样本开始，生成器将合成网络和 ICI 的集合，平均而言，它将在其结构的多个尺度上保留一组不同的拓扑和物理设计属性。这些属性将包括中心性、分类性、路径长度、聚类、流、所需物理能力和模块化的多种度量。这种方法在多个尺度上引入了整个集合中许多属性的无偏变异性，从而创建了合成系统所需的真实感。这些模型将包括拓扑和物理设计中纳入的人为因素组件。将开发用于生成基础设施和 ICI 合成网络的算法和启发式工具箱，并传播生成的基准。这项工作的成果将有助于通过网络生成算法的根本进步实现 ICI 的模拟、策略测试和决策等任务。该工具箱将采用模块化方法设计，使其能够发展并应用于其他领域。由计算机科学、行为科学、土木工程、网络科学和公共卫生领域的专业知识组成的跨学科研究团队将理想地支持现实合成数据生成的目标。来自网络分析、大数据系统、机器学习、水网络和组织科学的观点和方法将被用来开发一个方法工具包，其中包括网络分析、优化和统计分析技术的组合。由此产生的产品将包括开发的算法和生成的合成数据集，以供广泛的科学界传播。

项目成果

Multiscale planar graph generation

• DOI: 10.1007/s41109-019-0142-3
• 发表时间: 2018-02
• 期刊: Applied Network Science
• 影响因子: 2.2
• 作者: Varsha Chauhan;Alexander Gutfraind;Ilya Safro