Community detection in sparse latent space models

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.

References in corpus (6)

• Community detection in graphs
• Social Structure of Facebook Networks
• Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
• Modeling homophily and stochastic equivalence in symmetric relational data
• Community detection with nodal information
• Generalized linear models with low rank effects for network data