Approximating the Average-Case Graph Search Problem with Non-Uniform Costs
This work provides the first approximation algorithms for the vertex query variant of the graph search problem with non-uniform costs, advancing theoretical understanding for algorithm designers.
The paper addresses the NP-hard problem of finding a hidden target in a graph with non-uniform query costs and vertex weights, aiming to minimize the weighted average-case cost. They provide an O(√log n)-approximation algorithm for general graphs and a (4+ε)-approximation for trees.
Consider the following generalization of the classic binary search problem: A searcher is required to find a hidden target vertex $x$ in a graph $G$. To do so, they iteratively perform queries to an oracle, each about a chosen vertex $v$. After each such call, the oracle responds whether the target was found and if not, the searcher receives as a reply the connected component in $G-v$ which contains $x$. Additionally, each vertex $v$ may have a different query cost $c(v)$ and a different weight $w(v)$. The goal is to find the optimal querying strategy which minimizes the weighted average-case cost required to find $x$. The problem is NP-hard even for uniform weights and query costs. Inspired by the progress on the edge query variant of the problem [SODA '17], we establish a connection between searching and vertex separation. By doing so, we provide an $O(\sqrt{\log n})$-approximation algorithm for general graphs and a $(4+ε)$-approximation algorithm for the case when the input is a tree.