Community detection in sparse latent space models
This provides a theoretical guarantee for community detection in sparse networks, which is incremental as it extends existing methods to a broader model class.
The paper tackles the problem of community detection in sparse latent space models, showing that a simple spectral clustering algorithm with local refinement achieves consistency and optimality for a broad class of models, including latent eigenmodels.
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.