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Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network

Fig 2

IRN and resilience curves under complete failure and recovery.

A. The IRN is displayed. The 12-largest communities, each of which map to a color, capture the vast majority (91.6%) of the stations. Stations are sized by traffic volume. B. (Left) We quantify the robustness of IRN as it responds to random versus intentional attacks, where intentional attacks are motivated by either railway station connectivity (degree) or traffic volume (strength). For intentional attacks, approximately 20% of stations must be disrupted for the full IRN to lose all critical functionality, as measured with SCF. (Right) Using the same metric SCF, recovery strategies that propose alternative prioritizations for recovery of stations are compared, using an N = 1000 ensemble of randomly generated sequences as a baseline. Number of connections (degree) and traffic volume (strength) are used as intuitive measures for generating recovery sequences, and the results are plotted. In addition, betweenness, Eigenvector, and closeness centrality are used, and the results are plotted.

Fig 2