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Bayesian Model Selection of Stochastic Block Models
A central problem in analyzing networks is partitioning them into modules or communities.
Scalable Text and Link Analysis with Mixed-Topic Link Models
The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm.
Model Selection for Degree-corrected Block Models
We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.
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