13f320e7b5ead1024ac95c3b208610db-Reviews.html

The paper introduces a probabilistic model for networks which assigns each node in the network to multiple, overlapping latent communities. Inference is done using a stochastic variational method and the experimental evaluations are performed on very large networks. The first thing I note is that you do not cite Morup et al. (2010) "Infinite multiple membership relational modelling for complex networks", which in truth was the first work to perform inference for a latent feature relational model on large datasets -- in effect, rendering your statement on 067-068 "... these innovations allow the first..." incorrect. This is a rather serious oversight, because their paper not only performs large scale inference, but their method is also an MCMC method, which is well-known to usually produce more accurate results than variational methods. I believe the strongest contribution from this paper is the application of a stochastic variational inference method to a relational data model.