Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network
The top row schematically illustrates portions of the IRN initially impacted by realistic natural and cyber or cyber-physical threat scenarios, all with the same initial network topology as shown in Fig 2A, where 752 stations reside in the largest giant component (SCF = 1). The bottom row displays the community structure post hazard in each case. A. The impact of a disaster with properties similar to that of the December 2004 Indian Ocean tsunami is displayed. As suggested by the insight that communities are relatively independent as obtained from Fig 2, the regional nature of the hazard (shaded blue, top) significantly impacts the Southeastern coast, removing 28 stations. The number of communities increases from 49 to 75. Yet, the structure of the remainder of the network remains relatively intact (SCF = 0.903, where 679 stations remain in the giant component, see Methods and Data). B. For a simulated cyber or cyber-physical attack scenario, where 19 stations are perhaps maliciously targeted based on traffic volume (nodes shaded grey, top) and removed, the network structure is fractured significantly (SCF = 0.890, where 669 stations remain in the giant component). The number of communities increases from 49 to 96. C. A scenario based on a cascade from the power grid, similar to the 2012 blackout (shaded grey, top) is also simulated. The impact is significant, removing 39 stations, but the degradation of the IRN remains regionally contained (SCF = 0.852, where 641 stations remain in the giant component). The number of communities increases from 49 to 102. Note that differences that appear relatively in the SCF can have significant practical implications with a large network. In the case of the IRN given TF = 752, an SCF dropping by about 0.01 means 10 less stations are part of the giant component.