English

New Greedy Spanners and Applications

Data Structures and Algorithms 2026-03-19 v1

Abstract

We present a simple greedy procedure to compute an (α,β)(\alpha,\beta)-spanner for a graph GG. We then show that this procedure is useful for building fault-tolerant spanners, as well as spanners for weighted graphs. Our first main result is an algorithm that, given a multigraph GG, outputs an ff edge fault-tolerant (k,k1)(k,k-1)-spanner HH of size O(fn1+1k)O(fn^{1+\frac1k}) which is tight. To our knowledge, this is the first tight result concerning the price of fault tolerance in spanners which are not multiplicative, in any model of faults. Our second main result is a new construction of a spanner for weighted graphs. We show that any weighted graph GG has a subgraph HH with O(n1+1k)O(n^{1+\frac{1}{k}}) edges such that any path PP of hop-length \ell in GG has a replacement path PP' in HH of weighted length w(P)+(2k2)w(1/2)(P)\leq w(P)+(2k-2)w^{(1/2)}(P) where w(P)w(P) is the total edge weight of PP, and w(1/2)w^{(1/2)} denotes the sum of the largest 2\lceil \frac{\ell}{2} \rceil edge weights along PP. Moreover, we show such approximation is optimal for shortest paths of hop-length 22. To our knowledge, this is the first construction of a spanner for weighted graphs that strictly improves upon the stretch of multiplicative (2k1)(2k-1)-spanners for all non-adjacent vertex pairs, while maintaining the same size bound. Our technique is based on using clustering and ball-growing, which are methods commonly used in designing spanner algorithms, to analyze simple greedy algorithms. This allows us to combine the flexibility of clustering approaches with the unique properties of the greedy algorithm to get improved bounds. In particular, our methods give a very short proof that the parallel greedy spanner adds O(kn1+1k)O(kn^{1+\frac{1}{k}}) edges, improving upon known bounds.

Keywords

Cite

@article{arxiv.2603.17085,
  title  = {New Greedy Spanners and Applications},
  author = {Elizaveta Popova and Elad Tzalik},
  journal= {arXiv preprint arXiv:2603.17085},
  year   = {2026}
}

Comments

ITCS '26

R2 v1 2026-07-01T11:25:05.450Z