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INFOCOM
2003
IEEE

Big-Bang Simulation for embedding network distances in Euclidean space

12 years 1 months ago
Big-Bang Simulation for embedding network distances in Euclidean space
— Embedding of a graph metric in Euclidean space efficiently and accurately is an important problem in general with applications in topology aggregation, closest mirror selection, and application level routing. We propose a new graph embedding scheme called Big-Bang Simulation (BBS), which simulates an explosion of particles under force field derived from embedding error. BBS is shown to be significantly more accurate, compared to all other embedding methods including GNP. We report an extensive simulation study of BBS compared with several known embedding scheme and show its advantage for distance estimation (as in the IDMaps project), mirror selection and topology aggregation.
Yuval Shavitt, Tomer Tankel
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where INFOCOM
Authors Yuval Shavitt, Tomer Tankel
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