NeurIPS 2019poster45 citations
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
Abstract
Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the Bethe-Hessian matrix H
BibTeX
@inproceedings{NEURIPS2019_3e6260b8,
author = {Dall\textquotesingle Amico, Lorenzo and Couillet, Romain and Tremblay, Nicolas},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/3e6260b81898beacda3d16db379ed329-Paper.pdf},
volume = {32},
year = {2019}
}