  Many real-world networks contain rich textual information in the edges, such
as email networks where an edge between two nodes is an email exchange. Other
examples include co-author networks and social media networks. The useful
textual information carried in the edges is often discarded in most network
analyses, resulting in an incomplete view of the relationships between nodes.
In this work, we propose to represent the text document between each pair of
nodes as a vector counting the appearances of keywords extracted from the
corpus, and introduce a new and flexible preferential latent space network
model that can offer direct insights on how contents of the textual exchanges
modulate the relationships between nodes. We establish identifiability
conditions for the proposed model and tackle model estimation with a
computationally efficient projected gradient descent algorithm. We further
derive the non-asymptotic error bound of the estimator from each step of the
algorithm. The efficacy of our proposed method is demonstrated through
simulations and an analysis of the Enron email network.
