Efficient computation of target-oriented link criticalness centrality in uncertain graphs

We challenge the problem of efficiently identifying critical links that substantially degrade network performance if they do not function under a realistic situation where each link is probabilistically disconnected, e.g., unexpected traffic accident in a road network and unexpected server down in a communication network. To solve this problem, we utilize the bridge detection technique in graph theory and efficiently identify critical links in case the node reachability is taken as the performance measure.To be more precise, we define a set of target nodes and a new measure associated with it, Target-oriented latent link Criticalness Centrality (TCC), which is defined as the marginal loss of the expected number of nodes in the network that can reach, or equivalently can be reached from, one of the target nodes, and compute TCC for each link by use of detected bridges. We apply the proposed method to two real-world networks, one from social network and the other from spatial network, and empirically show that the proposed method has a good scalability with respect to the network size and the links our method identified possess unique properties. They are substantially more critical than those obtained by the others, and no known measures can replace the TCC measure.

Simple method for identifying interdependencies in service delivery in critical infrastructure networks

Critical infrastructure failures from natural hazard events affect the economic and social well-being of communities. This is particularly true in lower income countries, where infrastructure may be less resistant to natural hazards and disaster recovery is often limited by available resources. The interconnectivity of these systems can strongly affect the services they deliver, and the failure of one infrastructure system can result in cascade failures with wide-reaching consequences. Unfortunately, interconnectivity has been particularly difficult to measure. We present a method for identifying service-oriented interdependencies in interconnected networks. The approach uses well-established methods for network analysis and is demonstrated for healthcare services in the Commonwealth of Dominica, a small island state in the Caribbean. We show that critical links in road networks necessary for healthcare service delivery are important for more than just patient access to a facility, but also on the supply chains that enable the hospitals to function (e.g., water, fuel, medicine). Once identified, the critical links can be overlaid with known hazard vulnerabilities to identify the infrastructure segments of highest priority, based on the risk and consequences of failure. An advantage of the approach presented is that it requires relatively little input data when compared to many network prioritization models and can be run using open-source geospatial data such as OpenStreetMap. The method can be expanded beyond road networks to assess the service-oriented criticality of any infrastructure network.