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Abstract : |
Embedding of a graph metric in Euclidean space e-ciently 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 BigBang Simulation (BBS), which simulates an explosion of particles under force eld derived from embedding error. BBS is shown to be signicantly 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., |
