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Graph partitioning via adaptive spectral techniques
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An Efficient Sparse Regularity Concept
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An adaptive spectral heuristic for partitioning random graphs
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
Bounding the misclassification error in spectral partitioning in the planted partition model
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Reconstructing many partitions using spectral techniques
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Graph Bisection is the problem of partitioning the vertices of a graph into two equal-size pieces so as to minimize the number of edges between the two pieces. This paper presents an algorithm that will, for almost all graphs in a certain class, output the minimum-size bisection. Furthermore the algorithm will yield, for almost all such graphs, a proof that the bisection is optimal. The algorithm is based on computing eigenvalues and eigenvectors of matrices associated with the graph.