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Andrés D. González

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Mapping Network States Using Connectivity Queries

Dec 24, 2020
Alexander Rodríguez, Bijaya Adhikari, Andrés D. González, Charles Nicholson, Anil Vullikanti, B. Aditya Prakash

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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.

* Appears in IEEE BigData 2020. Preliminary version in NeurIPS 2020 AI + HADR 
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