Spectral inference for large Stochastic Blockmodels with nodal covariates

Understand

In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure.

• To this end, we develop spectral estimators for both unobserved blocks and the effect of covariates in stochastic blockmodels.
• On the theoretical side, we establish asymptotic normality of our estimators for the subsequent purpose of performing inference.
• On the applied side, we show that computing our estimator is much faster than standard variational expectation--maximization algorithms and scales well for large networks.