Distribution-Sensitive Construction of the Greedy Spanner

TL;DR

This paper introduces a new, faster method for constructing the greedy spanner in geometric graphs, leveraging local properties and probabilistic analysis to achieve near-linear expected running time on uniform point sets.

Contribution

It provides the first subquadratic time algorithms for greedy spanner construction and recognition on uniform point sets, using a novel local characterization of t-spanners.

Findings

Significant performance improvements over existing algorithms on real and synthetic data.

Proven near-linear expected time bound for uniform point sets.

First subquadratic algorithms for greedy spanner construction and recognition without assumptions on E.

Abstract

The greedy spanner is the highest quality geometric spanner (in e.g. edge count and weight, both in theory and practice) known to be computable in polynomial time. Unfortunately, all known algorithms for computing it take Omega(n^2) time, limiting its applicability on large data sets. We observe that for many point sets, the greedy spanner has many `short' edges that can be determined locally and usually quickly, and few or no `long' edges that can usually be determined quickly using local information and the well-separated pair decomposition. We give experimental results showing large to massive performance increases over the state-of-the-art on nearly all tests and real-life data sets. On the theoretical side we prove a near-linear expected time bound on uniform point sets and a near-quadratic worst-case bound. Our bound for point sets drawn uniformly and independently at random in a square follows from a local characterization of t-spanners we give on such point sets: we give a geometric property that holds with high probability on such point sets. This property implies that if an edge set on these points has t-paths between pairs of points `close' to each other, then it has t-paths between all pairs of points. This characterization gives a O(n log^2 n log^2 log n) expected time bound on our greedy spanner algorithm, making it the first subquadratic time algorithm for this problem on any interesting class of points. We also use this characterization to give a O((n + |E|) log^2 n log log n) expected time algorithm on uniformly distributed points that determines if E is a t-spanner, making it the first subquadratic time algorithm for this problem that does not make assumptions on E.