A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures

TL;DR

This paper introduces a scalable Bayesian method to reconstruct interdependent infrastructure networks from cascading failure data, addressing privacy issues and incomplete information.

Contribution

It presents a nonparametric Bayesian approach with an efficient sampling algorithm to accurately infer network topology from failure observations.

Findings

Outperforms existing methods in accuracy and speed

Successfully reconstructs synthetic and real-world infrastructure networks

Demonstrates broad applicability across different systems

Abstract

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.