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 The first thing I note is that you do not cite Morup et al.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.

In this abstract, we compare semantic search (in the RDF model) with keyword search (in the relational model), and illustrate how these two search paradigms are different. This comparison addresses the following questions: (1) What can semantic search achieve that keyword search can not (in terms of behavior)? (2) Why is it difficult to simulate semantic search, using keyword search on the relational data model? We use the term keyword search, when the search is performed on data stored in the relational data model, as in traditional relational databases, and an example of keyword search in databases is [Hri02]. We use the term semantic search, when the search is performed on data stored in the RDF data model. Note that when the data is modeled in RDF, it inherently contains explicit typed relations or semantics, and hence the use of the term “semantic search.” Let us begin with an example, to illustrate the differences between semantic search and keyword search.