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Https://proceedings. Neurips.cc/paper/202 0/file/0a5052334511e 344f15ae0bfafd47a67- Paper.pdf

0a5052334511e344f15ae0bfafd47a67-Paper.pdf
Preview of https://proceedings. neurips.cc/paper/202 0/file/0a5052334511e 344f15ae0bfafd47a67- Paper.pdf
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Researchers propose a spectral clustering algorithm for directed graphs, introducing a complex-valued representation of the graph Laplacian matrix to capture higher-order structural information among clusters. The algorithm computes an approximate partition with a bounded approximation guarantee in nearly-linear time and can be implemented in sub-linear time under certain assumptions. Experimental results on the UN Comtrade dataset demonstrate the significance of the work, exhibiting how clusters of countries relate to each other with respect to import and export records and how these clusters evolve over time.

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Researchers propose a spectral clustering algorithm for directed graphs, introducing a complex-valued representation of the graph Laplacian matrix to capture...

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  • File Size: 1.19 MB
  • Pages: 11
  • Language: EN
  • Total Downloads: 50
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