On the Power of Louvain in the Stochastic Block Model

Abstract

A classic problem in machine learning and data analysis is to partition the vertices of a network in such a way that vertices in the same set are densely connected and vertices in different sets are loosely connected.

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Cite

Text

Cohen-Addad et al. "On the Power of Louvain in the Stochastic Block Model." Neural Information Processing Systems, 2020.

Markdown

[Cohen-Addad et al. "On the Power of Louvain in the Stochastic Block Model." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/cohenaddad2020neurips-power/)

BibTeX

@inproceedings{cohenaddad2020neurips-power,
  title     = {{On the Power of Louvain in the Stochastic Block Model}},
  author    = {Cohen-Addad, Vincent and Kosowski, Adrian and Mallmann-Trenn, Frederik and Saulpic, David},
  booktitle = {Neural Information Processing Systems},
  year      = {2020},
  url       = {https://mlanthology.org/neurips/2020/cohenaddad2020neurips-power/}
}