EAGER: SSDIM: Multiscale Methods for Generating Infrastructure Networks

Clemson University, Clemson SC

Investigators

Abstract

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.