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.

本研究结合自然语言处理、机器学习、社交网络分析与网络科学等视角，运用图神经网络分析了 J.R.R. Tolkien 的作品中人物关系网络，并发现这种分析方法对分析和可视化 Tolkien 文学作品的叙述风格有显著帮助，并且在角色分类、嵌入和共现预测等方面，图神经网络优于其他常见的词嵌入方法。该研究突出了图学习在计算文学研究中的潜在大有用途。