You are currently offline. Some features of the site may not work correctly.

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}
}

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