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.

翻译：本研究通过构建多维相似性网络，形式化了一种计算模型以模拟古典波斯诗人之间的影响动态。基于Ganjoor语料库精心整理的数据集，我们利用语义、词汇、文体、主题及韵律特征来界定每位诗人的作品集。这些特征被纳入加权相似性矩阵，进而聚合生成展示诗人间影响关系的综合图谱。通过计算度中心性、接近中心性、中介中心性、特征向量中心性与Katz中心性等指标，我们进一步开展网络分析以识别核心诗人、风格枢纽及桥梁诗人。此外，为获得类型学洞见，我们采用Louvain社区检测算法划分出兼具风格与主题一致性的诗人集群，这些集群与萨布克-印迪风格、萨布克-呼罗珊风格及文学回归运动等公认文学流派高度吻合。我们的研究为波斯文学提供了数据驱动的新视角，区分了经典地位与文本间影响力，从而揭示了那些相对不知名却具有重要结构意义的诗人。通过将计算语言学与文学研究相结合，本文构建了一个可解释、可扩展的诗歌传统分析模型，既支持数字人文学科的历史反思，也为前瞻性研究奠定基础。