NAZM: network analysis of zonal petrics in Persian poetic tradition

This research presents a computational model designed to analyze influence dynamics between classical Persian poets in producing a multi-dimensional similarity network. Using a hand-curated dataset derived from Ganjoor’s corpus, we integrate semantic, lexical, stylistic, thematic, and metrical features generating weighted similarity matrices subsequently integrated into an influence graph portraying relationships between poets. A network analysis identifies both famous poets such as Hafez, Saʿdi, and Rumi holding central positions alongside minor poets such as Khāqānī and Salmān Sāvajī whose brokerage roles become structurally informative. By using community detection via the Louvain algorithm, we further connect discovered groups to known literary schools, notably Sabk-e Khorasani, Sabk-e Hindi, and the Bazgasht-e Adabi move ment. The findings exemplify how data-driven methods can differentiate between canonical height and structural influence and hence yield new insights into the evolution of Persian poetic lineages. By integrating computational linguistics and literary analysis, our proposed outline presents a scalable and interpretable instrument suited to both historical literary studies and future digital humanities work. Throughout, we use multi-dimensional similarity as a proxy for potential influence. Centrality therefore reflects structural integration within the network rather than cultural magnitude or causal primacy; establishing directionality requires temporal and historical evidence.