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Low distortion spanners

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A spanner of an undirected unweighted graph is a subgraph that approximates the distance metric of the original graph with some specified accuracy. Specifically, we say H ⊆ G is an f -spanner of G if any two vertices u , v at distance d in G are at distance at most f ( d ) in H . There is clearly some trade-off between the sparsity of H and the distortion function f , though the nature of the optimal trade-off is still poorly understood. In this article we present a simple, modular framework for constructing sparse spanners that is based on interchangable components called connection schemes . By assembling connection schemes in different ways we can recreate the additive 2- and 6-spanners of Aingworth et al. [1999] and Baswana et al. [2009], and give spanners whose multiplicative distortion quickly tends toward 1. Our results rival the simplicity of all previous algorithms and provide substantial improvements (up to a doubly exponential reduction in edge density) over the comparable spanners of Elkin and Peleg [2004] and Thorup and Zwick [2006].
Association for Computing Machinery (ACM)
Title: Low distortion spanners
Description:
A spanner of an undirected unweighted graph is a subgraph that approximates the distance metric of the original graph with some specified accuracy.
Specifically, we say H ⊆ G is an f -spanner of G if any two vertices u , v at distance d in G are at distance at most f ( d ) in H .
There is clearly some trade-off between the sparsity of H and the distortion function f , though the nature of the optimal trade-off is still poorly understood.
In this article we present a simple, modular framework for constructing sparse spanners that is based on interchangable components called connection schemes .
By assembling connection schemes in different ways we can recreate the additive 2- and 6-spanners of Aingworth et al.
[1999] and Baswana et al.
[2009], and give spanners whose multiplicative distortion quickly tends toward 1.
Our results rival the simplicity of all previous algorithms and provide substantial improvements (up to a doubly exponential reduction in edge density) over the comparable spanners of Elkin and Peleg [2004] and Thorup and Zwick [2006].

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