  Due to the significant increase of communications between individuals via
social media (Facebook, Twitter, Linkedin) or electronic formats (email, web,
e-publication) in the past two decades, network analysis has become a
unavoidable discipline. Many random graph models have been proposed to extract
information from networks based on person-to-person links only, without taking
into account information on the contents. This paper introduces the stochastic
topic block model (STBM), a probabilistic model for networks with textual
edges. We address here the problem of discovering meaningful clusters of
vertices that are coherent from both the network interactions and the text
contents. A classification variational expectation-maximization (C-VEM)
algorithm is proposed to perform inference. Simulated data sets are considered
in order to assess the proposed approach and to highlight its main features.
Finally, we demonstrate the effectiveness of our methodology on two real-word
data sets: a directed communication network and a undirected co-authorship
network.
