Fast approximation and exact computation of negative curvature parameters of graphs

TL;DR

This paper introduces a new characterization of graph hyperbolicity, enabling efficient approximation and exact computation of hyperbolicity and related parameters, with significant implications for large-scale network analysis.

Contribution

It provides a simple factor 8 approximation algorithm for graph hyperbolicity in optimal quadratic time and presents the first efficient algorithms for exact computation of related parameters.

Findings

Approximation algorithm achieves factor 8 in O(n^2) time.

Algorithms for exact computation of hyperbolicity and related parameters.

Characterization extends to geodesic metric spaces with spanning trees.

Abstract

In this paper, we study Gromov hyperbolicity and related parameters, that represent how close (locally) a metric space is to a tree from a metric point of view. The study of Gromov hyperbolicity for geodesic metric spaces can be reduced to the study of graph hyperbolicity. The main contribution of this paper is a new characterization of the hyperbolicity of graphs. This characterization has algorithmic implications in the field of large-scale network analysis. A sharp estimate of graph hyperbolicity is useful, e.g., in embedding an undirected graph into hyperbolic space with minimum distortion [Verbeek and Suri, SoCG'14]. The hyperbolicity of a graph can be computed in polynomial-time, however it is unlikely that it can be done in subcubic time. This makes this parameter difficult to compute or to approximate on large graphs. Using our new characterization of graph hyperbolicity, we provide a simple factor 8 approximation algorithm for computing the hyperbolicity of an $n$-vertex graph $G=(V,E)$ in optimal time $O(n^2)$ (assuming that the input is the distance matrix of the graph). This algorithm leads to constant factor approximations of other graph-parameters related to hyperbolicity (thinness, slimness, and insize). We also present the first efficient algorithms for exact computation of these parameters. All of our algorithms can be used to approximate the hyperbolicity of a geodesic metric space. We also show that a similar characterization of hyperbolicity holds for all geodesic metric spaces endowed with a geodesic spanning tree. Along the way, we prove that any complete geodesic metric space $(X,d)$ has such a geodesic spanning tree. We hope that this fundamental result can be useful in other contexts.