Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another crucial aspect: the textual context in which characters interact. We introduce Dynamic Heterogeneous Character Networks (DHCNs), which organize long novels into temporally localized heterogeneous graphs that align characters with their textual contexts. We extract around 20,000 DHCNs from Project Gutenberg, and propose GraphLit, a self-supervised learning framework that learns rich literary representations through a masked graph autoencoder objective. Across a wide-range of 12 character-related tasks, GraphLit improves over text-only and graph-only baselines, particularly on tasks requiring contextual understanding. Finally, we demonstrate the applicability of DHCNs and GraphLit for literary analysis by studying the link between narrative non-linearity and dynamic social features.
GraphLit：面向文学研究的文本增强动态人物关系网络表示学习 / GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
本文提出了一种名为GraphLit的自监督学习框架，能够将长篇小说中的角色及其对话文本动态组织成随时间变化的关系网络，从而在角色分类、关系预测等十二项任务上显著优于仅用文本或仅用图结构的方法，尤其擅长理解需要上下文分析的任务。
源自 arXiv: 2605.28643
