Contextual Stochastic Block Models
Social and Information Networks
2018-07-26 v1 Machine Learning
Machine Learning
Abstract
We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in the detection of latent community structure without nodes covariates and a large body of empirical work using diverse heuristics for combining node covariates with graphs for inference. The tightness of our analysis implies in particular, the information theoretical necessity of combining the different sources of information. Our analysis holds for networks of large degrees as well as for a Gaussian version of the model.
Cite
@article{arxiv.1807.09596,
title = {Contextual Stochastic Block Models},
author = {Yash Deshpande and Andrea Montanari and Elchanan Mossel and Subhabrata Sen},
journal= {arXiv preprint arXiv:1807.09596},
year = {2018}
}
Comments
28 pages, 1 figure, conference submission