EAGER: SSDIM: Multiscale Methods for Generating Infrastructure Networks

这个探索性研究（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，该生成器可以捕获真实网络的许多特征，并包含任意大或小程度的随机性。从已知或假设的网络样本开始，生成器将合成网络和集成接口的集合，这些网络和集成接口将在其结构的多个尺度上平均保存一组不同的拓扑和物理设计特性。这些属性将包括中心性、分类性、路径长度、集群、流、所需的物理容量和模块化的几种度量。这种方法在多个尺度上引入了许多这些属性的无偏可变性，从而创建了合成系统的理想真实感。这些模型将包括在拓扑和物理设计中合并的人为因素组件。将开发一个用于生成基础设施和综合信息系统综合网络的算法和启发式工具箱，并将分发生成的基准。这项工作的成果将通过网络生成算法的基本进步促进ici的模拟、政策测试和决策制定等任务。该工具箱将采用模块化方法进行设计，使其能够发展并应用于其他领域。跨学科的研究团队包括计算机科学、行为科学、土木工程、网络科学和公共卫生方面的专业知识，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方法将被引入开发一个方法工具包，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，供广泛的科学界传播。

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.