A General Framework for Graph Sparsification

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

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Ramesh Hariharan, Debmalya Panigrahi
• Keywords: CORR 2010, Education, Graph Sparsification, Graph Sparsification Problem, Weighted Graphs