# Approximation algorithms and an integer program for multi-level graph spanners

## TL;DR

This paper introduces a multi-level graph spanner problem, generalizes subsetwise spanners, and provides an ILP formulation and heuristics for efficient approximation, with applications in network design and visualization.

## Contribution

It generalizes the subsetwise spanner problem to multi-level graphs and formulates a polynomial-size ILP for the pairwise spanner problem, addressing an open question.

## Key findings

- ILP formulation of size O(|E||V|^2) for pairwise spanner problem
- Heuristics and ILP tested on graphs with up to 100 vertices
- Applications demonstrated in network building and visualization

## Abstract

Given a weighted graph $G(V,E)$ and $t \ge 1$, a subgraph $H$ is a \emph{$t$--spanner} of $G$ if the lengths of shortest paths in $G$ are preserved in $H$ up to a multiplicative factor of $t$. The \emph{subsetwise spanner} problem aims to preserve distances in $G$ for only a subset of the vertices. We generalize the minimum-cost subsetwise spanner problem to one where vertices appear on multiple levels, which we call the \emph{multi-level graph spanner} (MLGS) problem, and describe two simple heuristics. Applications of this problem include road/network building and multi-level graph visualization, especially where vertices may require different grades of service. We formulate a 0--1 integer linear program (ILP) of size $O(|E||V|^2)$ for the more general minimum \emph{pairwise spanner problem}, which resolves an open question by Sigurd and Zachariasen on whether this problem admits a useful polynomial-size ILP. We extend this ILP formulation to the MLGS problem, and evaluate the heuristic and ILP performance on random graphs of up to 100 vertices and 500 edges.