A spectral method for community detection in moderately-sparse degree-corrected stochastic block models

Lennart Gulikers; Marc Lelarge; Laurent Massoulié

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arXiv · 1506.08621

A spectral method for community detection in moderately-sparse degree-corrected stochastic block models

Lennart Gulikers·Marc Lelarge·Laurent Massoulié

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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$(n)$ 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.

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Lennart Gulikers, Marc Lelarge, Laurent Massoulié. 2017-02-07. A spectral method for community detection in moderately-sparse degree-corrected stochastic block models. https://arxiv.org/abs/1506.08621

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