@misc{perri2022graph,
  abstract = {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.},
  added-at = {2023-02-20T18:03:26.000+0100},
  author = {Perri, Vincenzo and Qarkaxhija, Lisi and Zehe, Albin and Hotho, Andreas and Scholtes, Ingo},
  biburl = {https://www.bibsonomy.org/bibtex/2070ca503bc1201d2d04955b729b27dee/ifland},
  interhash = {d9ca320ad5969855cec637114e0324e3},
  intrahash = {070ca503bc1201d2d04955b729b27dee},
  keywords = {caidas-area-comp-lit-stud},
  note = {cite arxiv:2210.07871},
  timestamp = {2023-02-20T18:03:26.000+0100},
  title = {One Graph to Rule them All: Using NLP and Graph Neural Networks to
  analyse Tolkien's Legendarium},
  url = {http://arxiv.org/abs/2210.07871},
  year = 2022
}