  We are interested in the widespread problem of clustering documents and
finding topics in large collections of written documents in the presence of
metadata and hyperlinks. To tackle the challenge of accounting for these
different types of datasets, we propose a novel framework based on Multilayer
Networks and Stochastic Block Models. The main innovation of our approach over
other techniques is that it applies the same non-parametric probabilistic
framework to the different sources of datasets simultaneously. The key
difference to other multilayer complex networks is the strong unbalance between
the layers, with the average degree of different node types scaling differently
with system size. We show that the latter observation is due to generic
properties of text, such as Heaps' law, and strongly affects the inference of
communities. We present and discuss the performance of our method in different
datasets (hundreds of Wikipedia documents, thousands of scientific papers, and
thousands of E-mails) showing that taking into account multiple types of
information provides a more nuanced view on topic- and document-clusters and
increases the ability to predict missing links.
