A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures
This work addresses the challenge of understanding network interdependencies for critical infrastructure systems, where data is often unavailable, to anticipate cascading failures, though it is incremental as it builds on existing reconstruction methods.
The authors tackled the problem of reconstructing interdependent infrastructure network topologies from cascading failure observations, proposing a scalable nonparametric Bayesian approach that outperforms existing methods in accuracy and computational time.
Analyzing the behavior of complex interdependent networks requires complete information about the network topology and the interdependent links across networks. For many applications such as critical infrastructure systems, understanding network interdependencies is crucial to anticipate cascading failures and plan for disruptions. However, data on the topology of individual networks are often publicly unavailable due to privacy and security concerns. Additionally, interdependent links are often only revealed in the aftermath of a disruption as a result of cascading failures. We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures. Metropolis-Hastings algorithm coupled with the infrastructure-dependent proposal are employed to increase the efficiency of sampling possible graphs. Results of reconstructing a synthetic system of interdependent infrastructure networks demonstrate that the proposed approach outperforms existing methods in both accuracy and computational time. We further apply this approach to reconstruct the topology of one synthetic and two real-world systems of interdependent infrastructure networks, including gas-power-water networks in Shelby County, TN, USA, and an interdependent system of power-water networks in Italy, to demonstrate the general applicability of the approach.