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 scheme and show its advantage for distance estimation (as in the IDMaps project), mirror selection and topology aggregation.
| Original language | English |
|---|---|
| Pages (from-to) | 1922-1932 |
| Number of pages | 11 |
| Journal | Proceedings - IEEE INFOCOM |
| Volume | 3 |
| State | Published - 2003 |
| Event | 22nd Annual Joint Conference on the IEEE Computer and Communications Societies - San Francisco, CA, United States Duration: 30 Mar 2003 → 3 Apr 2003 |