Mapping Network States Using Connectivity Queries
This work is significant for infrastructure operators and disaster response teams, enabling quicker and more accurate damage assessment after disruptive events, which is crucial for recovery efforts.
This paper addresses the problem of inferring failed components in an infrastructure network after a disruption, given partial information about reachable nodes and a small sample of point probes. The authors formulate this problem using the Minimum Description Length (MDL) principle and propose a greedy algorithm that effectively minimizes the MDL cost.
Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identify failed components, especially the critical ones affecting the overall system performance.