NAZM: Network Analysis of Zonal Metrics in Persian Poetic Tradition

TL;DR

This paper introduces a computational network model to analyze influence and stylistic clusters among Persian poets, revealing structural significance of lesser-known figures and aligning with traditional literary schools.

Contribution

It presents a novel multi-dimensional similarity network model for Persian poetry, integrating semantic, lexical, stylistic, thematic, and metrical features for influence analysis.

Findings

Identifies key influence figures and stylistic hubs.

Detects poet clusters corresponding to literary schools.

Highlights lesser-known poets with significant structural influence.

Abstract

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.