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| Content Provider | ACM Digital Library |
|---|---|
| Author | Mehlhorn, Kurt Baswana, Surender Kavitha, Telikepalli Pettie, Seth |
| Copyright Year | 2010 |
| Abstract | An (α, β)-spanner of an unweighted graph $\textit{G}$ is a subgraph $\textit{H}$ that distorts distances in $\textit{G}$ up to a multiplicative factor of α and an additive term β. It is well known that any graph contains a (multiplicative) $(2\textit{k}™1,$ 0)-spanner of size $O(n^{1+1/k})$ and an (additive) (1,2)-spanner of size $O(n^{3/2}).$ However no other additive spanners are known to exist. In this article we develop a couple of new techniques for constructing (α, β)-spanners. Our first result is an additive (1,6)-spanner of size $O(n^{4/3}).$ The construction algorithm can be understood as an economical agent that assigns $\textit{costs}$ and $\textit{values}$ to paths in the graph, $\textit{purchasing}$ affordable paths and ignoring expensive ones, which are intuitively well approximated by paths already purchased. We show that this path buying algorithm can be parameterized in different ways to yield other sparseness-distortion tradeoffs. Our second result addresses the problem of which (α, β)-spanners can be computed efficiently, ideally in linear time. We show that, for any $\textit{k},$ a $(\textit{k},\textit{k}™1)-spanner$ with size $O(kn^{1+1/k})$ can be found in linear time, and, further, that in a distributed network the algorithm terminates in a constant number of rounds. Previous spanner constructions with similar performance had roughly twice the multiplicative distortion. |
| Starting Page | 1 |
| Ending Page | 26 |
| Page Count | 26 |
| File Format | |
| ISSN | 15496325 |
| e-ISSN | 15496333 |
| DOI | 10.1145/1868237.1868242 |
| Volume Number | 7 |
| Issue Number | 1 |
| Journal | ACM Transactions on Algorithms (TALG) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-12-08 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Spanner Metric embedding |
| Content Type | Text |
| Resource Type | Article |
| Subject | Mathematics |
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