Corpus ID: 220265800

Graph Laplacians, Riemannian Manifolds and their Machine-Learning

@article{He2020GraphLR,
  title={Graph Laplacians, Riemannian Manifolds and their Machine-Learning},
  author={Yanghui He and Shing-Tung Yau},
  journal={arXiv: Combinatorics},
  year={2020}
}
  • Yanghui He, Shing-Tung Yau
  • Published 2020
  • Mathematics, Physics
  • arXiv: Combinatorics
  • Graph Laplacians as well as related spectral inequalities and (co-)homology provide a foray into discrete analogues of Riemannian manifolds, providing a rich interplay between combinatorics, geometry and theoretical physics. We apply some of the latest techniques in data science such as supervised and unsupervised machine-learning and topological data analysis to the Wolfram database of some 8000 finite graphs in light of studying these correspondences. Encouragingly, we find that neural… CONTINUE READING
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    References

    SHOWING 1-10 OF 81 REFERENCES