  Natural Language Processing and Machine Learning have considerably advanced
Computational Literary Studies. Similarly, the construction of co-occurrence
networks of literary characters, and their analysis using methods from social
network analysis and network science, have provided insights into the micro-
and macro-level structure of literary texts. Combining these perspectives, in
this work we study character networks extracted from a text corpus of J.R.R.
Tolkien's Legendarium. We show that this perspective helps us to analyse and
visualise the narrative style that characterises Tolkien's works. Addressing
character classification, embedding and co-occurrence prediction, we further
investigate the advantages of state-of-the-art Graph Neural Networks over a
popular word embedding method. Our results highlight the large potential of
graph learning in Computational Literary Studies.
