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Analyzing the behavior of complex interdependent networks requires completeinformation about the network topology and the interdependent links acrossnetworks. For many applications such as critical infrastructure systems,understanding network interdependencies is crucial to anticipate cascadingfailures and plan for disruptions. However, data on the topology of individualnetworks are often publicly unavailable due to privacy and security concerns.Additionally, interdependent links are often only revealed in the aftermath ofa disruption as a result of cascading failures. We propose a scalablenonparametric Bayesian approach to reconstruct the topology of interdependentinfrastructure networks from observations of cascading failures.Metropolis-Hastings algorithm coupled with the infrastructure-dependentproposal are employed to increase the efficiency of sampling possible graphs.Results of reconstructing a synthetic system of interdependent infrastructurenetworks demonstrate that the proposed approach outperforms existing methods inboth accuracy and computational time. We further apply this approach toreconstruct the topology of one synthetic and two real-world systems ofinterdependent infrastructure networks, including gas-power-water networks inShelby County, TN, USA, and an interdependent system of power-water networks inItaly, to demonstrate the general applicability of the approach.

A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures