Big-Bang Simulation for Embedding Network Distances in Euclidean Space

DMCA

Big-Bang Simulation for Embedding Network Distances in Euclidean Space (2004)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Yuval Shavitt , Tomer Tankel

Citations:

151 - 4 self

BibTeX

@MISC{Shavitt04big-bangsimulation,
    author = {Yuval Shavitt and Tomer Tankel},
    title = {Big-Bang Simulation for Embedding Network Distances in Euclidean Space },
    year = {2004}
}

OpenURL

Abstract

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 schemes and show its advantage for distance estimation (as in the IDMaps project), mirror selection and topology aggregation.

Keyphrases

big-bang simulation euclidean space embedding network distance topology aggregation mirror selection idmaps project application level force field distance estimation important problem extensive simulation study new graph

DMCA