用角色互动图分析乌尔都小说叙事结构，识别作者风格。
Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks
- 将小说转为角色共现图，用图神经网络捕捉叙事结构
- 在7位作者52部小说上达85.7%作者识别准确率
- 为低资源语言文学分析提供新方法，适合文本风格研究者
传统作者分析多依赖词汇与风格特征，而对叙事结构的关注不足，尤其在乌尔都语等低资源语言中更为欠缺。本文提出一种基于图的框架，将乌尔都小说建模为角色互动网络，探索仅通过叙事结构能否推断作者风格。每部小说被表示为一个图，节点为角色，边代表角色在叙事邻近中的共现。系统比较了多种图表示方式，包括全局结构特征、节点级语义摘要、无监督图嵌入及有监督图神经网络。在包含52部乌尔都小说、7位作者的数据集上，学习得到的图表示显著优于手工设计和无监督基线，在严格的作者感知评估协议下最高达到0.857的准确率。
原文摘要 · Abstract (English)
Authorship analysis has traditionally focused on lexical and stylistic cues within text, while higher-level narrative structure remains underexplored, particularly for low-resource languages such as Urdu. This work proposes a graph-based framework that models Urdu novels as character interaction networks to examine whether authorial style can be inferred from narrative structure alone. Each novel is represented as a graph where nodes correspond to characters and edges denote their co-occurrence within narrative proximity. We systematically compare multiple graph representations, including global structural features, node-level semantic summaries, unsupervised graph embeddings, and supervised graph neural networks. Experiments on a dataset of 52 Urdu novels written by seven authors show that learned graph representations substantially outperform hand-crafted and unsupervised baselines, achieving up to 0.857 accuracy under a strict author-aware evaluation protocol.
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