Community detection in sparse latent space models
Machine Learning
2020-08-05 v1 Machine Learning
Statistics Theory
Methodology
Statistics Theory
Abstract
We show that a simple community detection algorithm originated from stochastic blockmodel literature achieves consistency, and even optimality, for a broad and flexible class of sparse latent space models. The class of models includes latent eigenmodels (arXiv:0711.1146). The community detection algorithm is based on spectral clustering followed by local refinement via normalized edge counting.
Cite
@article{arxiv.2008.01375,
title = {Community detection in sparse latent space models},
author = {Fengnan Gao and Zongming Ma and Hongsong Yuan},
journal= {arXiv preprint arXiv:2008.01375},
year = {2020}
}