Expander flows, geometric embeddings and graph partitioning
We give a O (√ log n)-approximation algorithm for the sparsest cut, edge expansion,
balanced separator, and graph conductance problems. This improves the O (log n)-
approximation of Leighton and Rao (1988). We use a well-known semidefinite relaxation
with triangle inequality constraints. Central to our analysis is a geometric theorem about
projections of point sets in R d, whose proof makes essential use of a phenomenon called
measure concentration. We also describe an interesting and natural “approximate�…
balanced separator, and graph conductance problems. This improves the O (log n)-
approximation of Leighton and Rao (1988). We use a well-known semidefinite relaxation
with triangle inequality constraints. Central to our analysis is a geometric theorem about
projections of point sets in R d, whose proof makes essential use of a phenomenon called
measure concentration. We also describe an interesting and natural “approximate�…
We give a O(√log n)-approximation algorithm for the sparsest cut, edge expansion, balanced separator, and graph conductance problems. This improves the O(log n)-approximation of Leighton and Rao (1988). We use a well-known semidefinite relaxation with triangle inequality constraints. Central to our analysis is a geometric theorem about projections of point sets in Rd, whose proof makes essential use of a phenomenon called measure concentration.
We also describe an interesting and natural “approximate certificate” for a graph's expansion, which involves embedding an n-node expander in it with appropriate dilation and congestion. We call this an expander flow.
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