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Graph partitioning into isolated, high conductance clusters: theory, computation and applications to preconditioning
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ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures table of contents
Munich, Germany
SESSION: Graph algorithms table of contents
Pages 137-145  
Year of Publication: 2008
ISBN:978-1-59593-973-9
Authors
Ioannis Koutis  Carnegie Mellon University, Pittsburgh, PA, USA
Gary L. Miller  Carnegie Mellon University, Pittsburgh, PA, USA
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

We consider the problem of decomposing a weighted graph with n vertices into a collection P of vertex disjoint clusters such that, for all clusters C ε P, the graph induced by the vertices in C and the edges leaving C, has conductance bounded below by φ. We show that for planar graphs we can compute a decomposition P such that |P| < n/ρ, where ρ is a constant, in O(log n) parallel time with O(n) work. Slightly worse guarantees can be obtained in nearly linear time for graphs that have fixed size minors or bounded genus. We show how these decompositions can be used in the first known linear work parallel construction of provably good preconditioners for the important class of fixed degree graph Laplacians. On a more theoretical note, we present upper bounds on the Euclidean distance of eigenvectors of the normalized Laplacian from the space of vectors which consists of the cluster-wise constant vectors scaled by the square roots of the total incident weights of the vertices.


REFERENCES

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Collaborative Colleagues:
Ioannis Koutis: colleagues
Gary L. Miller: colleagues