{"ID":2829296,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12654","arxiv_id":"2512.12654","title":"Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks","abstract":"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.","short_abstract":"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 whe...","url_abs":"https://arxiv.org/abs/2512.12654","url_pdf":"https://arxiv.org/pdf/2512.12654v1","authors":"[\"Hassan Mujtaba\",\"Hamza Naveed\",\"Hanzlah Munir\"]","published":"2025-12-14T11:59:16Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\",\"cs.SI\"]","methods":"[\"Graph Neural Network\"]","has_code":false}