Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915674772733952 |
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| author | Mujtaba, Hassan Naveed, Hamza Munir, Hanzlah |
| author_facet | Mujtaba, Hassan Naveed, Hamza Munir, Hanzlah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12654 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks Mujtaba, Hassan Naveed, Hamza Munir, Hanzlah Computation and Language Machine Learning Social and Information 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. |
| title | Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks |
| topic | Computation and Language Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2512.12654 |