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      Signed Laplacian for spectral clustering revisited

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          Abstract

          Classical spectral clustering is based on a spectral decomposition of a graph Laplacian, obtained from a graph adjacency matrix representing positive graph edge weights describing similarities of graph vertices. In signed graphs, the graph edge weights can be negative to describe disparities of graph vertices, for example, negative correlations in the data. Negative weights lead to possible negative spectrum of the standard graph Laplacian, which is cured by defining a signed Laplacian. We revisit comparing the standard and signed Laplacians and argue that the former is more natural than the latter, also showing that the negative spectrum is actually beneficial, for spectral clustering of signed graphs.

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          Normalized cuts and image segmentation

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            Partitioning Sparse Matrices with Eigenvectors of Graphs

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              Correlation Clustering

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                Author and article information

                Journal
                2017-01-05
                Article
                1701.01394
                7571c534-6e56-4ca3-a776-93d7012d4f8b

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                05C50, 05C70, 15A18, 58C40, 65F15, 65N25, 62H30, 91C20
                cs.DS cs.LG math.NA stat.ML

                Numerical & Computational mathematics,Data structures & Algorithms,Machine learning,Artificial intelligence

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