Numerical Comparisons of two Spectral Decompositions for Vertex Clustering
We study multi-way partitioning algorithms of a hypergraph which are based on its prior transformation into a geometric object by constructing a one-to-one mapping between the vertex set and a point set in a Euclidean space. The coordinates of the points are generated by a spectral decomposition of a positive semi-definite matrix. Here, we compare the decomposition of the discrete Laplacian of a graph associated with the hypergraph to that of the Torgerson matrix associated with a dissimilarity coefficient. Numerical results are presented on standard test cases of large sizes from the integrated circuit design literature.
KeywordsSpectral Decomposition Geometric Object Iterative Version Integrate Circuit Design Dissimilarity Coefficient
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