NFFT Meets Krylov Methods: Fast Matrix-Vector Products for the Graph Laplacian of Fully Connected Networks

@article{Alfke2018NFFTMK,
  title={NFFT Meets Krylov Methods: Fast Matrix-Vector Products for the Graph Laplacian of Fully Connected Networks},
  author={Dominik Alfke and D. Potts and M. Stoll and Toni Volkmer},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.04580}
}
The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network and is in certain applications densely populated. This makes computations, in particular matrix-vector products, with the graph Laplacian a hard task. A typical application is the computation of a number of its eigenvalues and eigenvectors. Standard methods become infeasible as the number of nodes in the graph is too… Expand
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  • M. Stoll
  • Computer Science, Mathematics
  • ArXiv
  • 2019

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