Generalized network recovery based on topology and optimization for real-world systems

TL;DR

This paper introduces a hybrid network recovery strategy that combines topology-based centrality measures and flow optimization techniques, demonstrated through real-world transportation case studies, enhancing resilience in critical systems.

Contribution

It develops a novel hybrid approach integrating complex network topology and optimization for improved recovery strategies in damaged systems.

Findings

Hybrid strategy outperforms individual methods in case studies

Performance depends on network attributes

Applicable to disaster management and climate adaptation

Abstract

Designing effective recovery strategies for damaged networked systems is critical to the resilience of built, human and natural systems. However, progress has been limited by the inability to bring together distinct philosophies, such as complex network topology through centrality measures and network flow optimization through entropy measures. Network centrality-based metrics are relatively more intuitive and computationally efficient while optimization-based approaches are more amenable to dynamic adjustments. Here we show, with case studies in real-world transportation systems, that the two distinct network philosophies can be blended to form a hybrid recovery strategy that is more effective than either, with the relative performance depending on aggregate network attributes. Direct applications include disaster management and climate adaptation sciences, where recovery of lifeline networks can save lives and economies.