EAGER: SSDIM: Multiscale Methods for Generating Infrastructure Networks

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，它可以捕获真实的网络的许多功能，并具有任意大小的随机性。从已知或假设网络的样本开始，生成器将合成网络和ICI的集合，这些集合平均而言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、非对称性、路径长度、集群、流量、所需物理容量和模块化的几个度量。这种方法在多个尺度下的许多这些属性中的整个集合中引入了无偏的可变性，这创建了合成系统的期望的真实性。这些模型将包括拓扑和物理设计中的人为因素组件。将开发用于生成基础设施和信息中心综合网络的算法和算法工具箱，并将传播生成的基准。这项工作的成果将促进这些任务，如模拟，政策测试和决策的ICI网络生成算法的根本进步。该工具箱将采用模块化方法设计，使其能够不断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专业人员组成的跨学科研究团队将为现实合成数据生成的目标提供理想的支持。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论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为广大科学界传播的已开发算法和生成的合成数据集。

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.