Optimal Laplacian regularization for sparse spectral community detection
Machine Learning
2020-05-18 v2 Machine Learning
Abstract
Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferable, but this point was left as a heuristic argument. In this paper we formally determine a proper regularization which is intimately related to alternative state-of-the-art spectral techniques for sparse graphs.
Keywords
Cite
@article{arxiv.1912.01419,
title = {Optimal Laplacian regularization for sparse spectral community detection},
author = {Lorenzo Dall'Amico and Romain Couillet and Nicolas Tremblay},
journal= {arXiv preprint arXiv:1912.01419},
year = {2020}
}