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Graph Partitioning for Low Threshold-Rank and Semi-Random Instances

August 7 @ 6:30 am - 7:30 am

In this talk, we will mainly focus on how the Sparsest Cut problem can be solved efficiently on a class of graphs called low threshold-rank graphs. The Sparsest Cut objective measures the sparsity of a cut in terms of edges going across the cut, normalized by the size of the partition. While the best known approximation guarantee for this problem is $O(sqrt{log n})$ in general, we show that one can do much better on graphs which already have a ‘clustered’ structure, using an efficient and intuitive algorithm. Our algorithm also furnishes a generalization of a geometric theorem of Goemans on embedding finite metric spaces into $ell_1$.Discipline/Coordinating Entity: Computer Science and Engineering


August 7
6:30 am - 7:30 am
Event Category:


6, Turkey