NAZM: Network Analysis of Zonal Metrics in Persian Poetic Tradition
This provides a data-driven approach for digital humanities to reinterpret Persian poetic tradition by distinguishing canonical significance from structural influence, though it applies existing computational methods to a new literary dataset.
This study developed a computational model to analyze influence dynamics among classical Persian poets by constructing a multi-dimensional similarity network using semantic, lexical, stylistic, thematic, and metrical features from the Ganjoor corpus, identifying key poets, style hubs, and bridging poets through network centrality measures and community detection that aligned with recognized literary schools.
This study formalizes a computational model to simulate classical Persian poets' dynamics of influence through constructing a multi-dimensional similarity network. Using a rigorously curated dataset based on Ganjoor's corpus, we draw upon semantic, lexical, stylistic, thematic, and metrical features to demarcate each poet's corpus. Each is contained within weighted similarity matrices, which are then appended to generate an aggregate graph showing poet-to-poet influence. Further network investigation is carried out to identify key poets, style hubs, and bridging poets by calculating degree, closeness, betweenness, eigenvector, and Katz centrality measures. Further, for typological insight, we use the Louvain community detection algorithm to demarcate clusters of poets sharing both style and theme coherence, which correspond closely to acknowledged schools of literature like Sabk-e Hindi, Sabk-e Khorasani, and the Bazgasht-e Adabi phenomenon. Our findings provide a new data-driven view of Persian literature distinguished between canonical significance and interextual influence, thus highlighting relatively lesser-known figures who hold great structural significance. Combining computational linguistics with literary study, this paper produces an interpretable and scalable model for poetic tradition, enabling retrospective reflection as well as forward-looking research within digital humanities.