2018/11/17 by Udit Bhatia, Bhatia, Udit, Lina Perelman +5
Business, Management and Accounting · Engineering · Environmental Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Analysis #Ecosystem dynamics and resilience #FOS: Physical sciences #Infrastructure Resilience and Vulnerability Analysis #Physics and Society (physics.soc-ph) #Statistics and Probability (physics.data-an) #Supply Chain Resilience and Risk Management #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1811.07242
arxiv created 2018/11/17 · openalex publication_date 2018/11/17 · arxiv updated 2018/11/20 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
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.