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because they result in degenerated matrix
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- Coverage 97.30% 97.25% -0.06%
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Is someone able to review this ? It seems good to me, but I'm not very knowledgeable in spectral graph theory |
gdalle
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Sep 14, 2023
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I don't understand the algorithm in detail but the changes are coherent with the PR description and they look good to me
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Fixes a few issues I have encountered when running
normalized_cut():eigs()returns less eigenvectors than requested, even less than 2. In the latter case the algorithm now would put all vertices to separate modules instead of throwing OutOfBounds exception.inv(D)). (Such situations should have been handled by the generalizedeigs(), but it is not implemented in ArnoldiMethod.jl, and IIUC using Arpack.jl is problematic).eigs()(handling real and complex cases uniformly)