Near-Linear Query Complexity for Graph Inference

TL;DR

This paper investigates the efficiency of reconstructing or verifying unknown graphs using distance or shortest path queries, achieving near-linear query complexity for various graph classes and problem settings.

Contribution

It introduces greedy algorithms for graph verification and reconstruction with near-linear query complexity, extending to graphs with bounded treewidth and chordal graphs.

Findings

Verification uses $n^{1+o(1)}$ distance queries.

Reconstruction with shortest path oracle also uses $n^{1+o(1)}$ queries.

For bounded treewidth and chordal graphs, query complexity is $ ilde O(n)$.

Abstract

How efficiently can we find an unknown graph using distance or shortest path queries between its vertices? Let $G = (V,E)$ be an unweighted, connected graph of bounded degree. The edge set $E$ is initially unknown, and the graph can be accessed using a \emph{distance oracle}, which receives a pair of vertices $(u,v)$ and returns the distance between $u$ and $v$. In the \emph{verification} problem, we are given a hypothetical graph $\hat G = (V,\hat E)$ and want to check whether $G$ is equal to $\hat G$. We analyze a natural greedy algorithm and prove that it uses $n^{1+o(1)}$ distance queries. In the more difficult \emph{reconstruction} problem, $\hat G$ is not given, and the goal is to find the graph $G$. If the graph can be accessed using a \emph{shortest path oracle}, which returns not just the distance but an actual shortest path between $u$ and $v$, we show that extending the idea of greedy gives a reconstruction algorithm that uses $n^{1+o(1)}$ shortest path queries. When the graph has bounded treewidth, we further bound the query complexity of the greedy algorithms for both problems by $\tilde O(n)$. When the graph is chordal, we provide a randomized algorithm for reconstruction using $\tilde O(n)$ distance queries.