Mapping Network States Using Connectivity Queries

TL;DR

This paper introduces a novel method to infer failed infrastructure components after a disaster using partial network information and a greedy algorithm based on the MDL principle, demonstrated on earthquake simulations.

Contribution

It formulates the problem of inferring network failures from partial data using MDL and proposes an effective greedy algorithm for practical damage assessment.

Findings

Successfully identifies critical failed components

Effective in real network earthquake simulations

Outperforms baseline methods

Abstract

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.