| Publisher | Microsoft | ||
|---|---|---|---|
| Format | 254.8KB PDF | Date added | 27 Mar 2007 |
| Topics | Knowledge and Data Management, Data Visualization | ||
| Downloads | 2 | ||
As networks grow large and complex, a key approach in managing information and constructing algorithms is to decompose the network into locality-preserving clusters. Then, information and/or management can be divided between the clusters, such that every node is responsible only for clusters for which it belongs. Such decompositions into locality sensitive clusters have become key tools in network and graph theory and span a large body of literature. This paper provides the first sparse covers and probabilistic partitions for graphs excluding a fixed minor that have strong diameter bounds; i.e. each set of the cover/partition has a small diameter as an induced sub-graph.
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