A spectral method for community detection in moderately-sparse degree-corrected stochastic block models
Probability
2017-02-09 v3 Machine Learning
Social and Information Networks
Machine Learning
Abstract
We consider community detection in Degree-Corrected Stochastic Block Models (DC-SBM). We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block-membership of all but a vanishing fraction of nodes, in the regime where the lowest degree is of order log or higher. Recovery succeeds even for very heterogeneous degree-distributions. The used algorithm does not rely on parameters as input. In particular, it does not need to know the number of communities.
Keywords
Cite
@article{arxiv.1506.08621,
title = {A spectral method for community detection in moderately-sparse degree-corrected stochastic block models},
author = {Lennart Gulikers and Marc Lelarge and Laurent Massoulié},
journal= {arXiv preprint arXiv:1506.08621},
year = {2017}
}