The covert set-cover problem with application to Network Discovery

TL;DR

This paper introduces a randomized algorithm for the covert set-cover problem that efficiently approximates the optimal cover with minimal queries and applies it to significantly improve algorithms for network discovery in graphs.

Contribution

It presents a novel Monte Carlo randomized algorithm for the covert set-cover problem with near-optimal query complexity and applies it to achieve exponential improvements in network discovery algorithms.

Findings

Achieves an $O( ext{OPT} imes ext{log}^2 N)$ query complexity for covert set-cover.

Provides an $O( ext{log}^2 n)$-competitive algorithm for network discovery.

Improves the competitive ratio from $ ext{Omega}( ext{sqrt}(n ext{log} n))$ to exponential levels.

Abstract

We address a version of the set-cover problem where we do not know the sets initially (and hence referred to as covert) but we can query an element to find out which sets contain this element as well as query a set to know the elements. We want to find a small set-cover using a minimal number of such queries. We present a Monte Carlo randomized algorithm that approximates an optimal set-cover of size $OPT$ within $O(\log N)$ factor with high probability using $O(OPT \cdot \log^2 N)$ queries where $N$ is the input size. We apply this technique to the network discovery problem that involves certifying all the edges and non-edges of an unknown $n$-vertices graph based on layered-graph queries from a minimal number of vertices. By reducing it to the covert set-cover problem we present an $O(\log^2 n)$-competitive Monte Carlo randomized algorithm for the covert version of network discovery problem. The previously best known algorithm has a competitive ratio of $\Omega (\sqrt{n\log n})$ and therefore our result achieves an exponential improvement.