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14

CORR
2010
Springer

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A General Framework for Graph Sparsification

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A General Framework for Graph Sparsification

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Given a weighted graph G and an error parameter > 0, the graph sparsification problem requires sampling edges in G and giving the sampled edges appropriate weights to obtain a sparse graph G with the following property: the weight of every cut in G is within a factor of (1

Ramesh Hariharan, Debmalya Panigrahi

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CORR 2010 | Education | Graph Sparsification | Graph Sparsification Problem | Weighted Graphs |

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Added	09 Dec 2010
Updated	09 Dec 2010
Type	Journal
Year	2010
Where	CORR
Authors	Ramesh Hariharan, Debmalya Panigrahi

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