Advances in Multilayer Network Modeling for Pressing Complex Problems

Multilayer network (MLN) models have been proposed as tools to assist in the analysis of complex systems in which entities interact across multiple types of relationships. Recent work at Sandia National Laboratories has focused on the application of MLNs to two pressing security problems. The first is analysis of high consequence facility security systems with the goal of modeling and understanding the unintended consequences of growing interdependencies in the digital age. The second focuses on understanding approximate equivalency of deterrence actions in the context of “Integrated Deterrence” in response to the growing risk of escalation across multiple domains. This talk focuses on the MLN models generated for these two projects and on the distinct statistical analyses used. We discuss how to fit and compare statistical MLN models, how they can be treated as inputs to predictive regression models, how MLN metrics have been analyzed in downstream analysis, and how inferential techniques can be applied directly to empirically derived networks. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525