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 a 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.