Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks
This addresses authorship analysis for low-resource languages like Urdu, though it is incremental as it applies existing graph methods to a new domain.
The paper tackled the problem of inferring authorial style from narrative structure in Urdu novels by modeling them as character interaction graphs, achieving up to 0.857 accuracy using graph neural networks.
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.