NAZM：波斯诗歌传统中区域度量学的网络分析
NAZM: Network Analysis of Zonal Metrics in Persian Poetic Tradition
AI总结：
本研究构建多维相似性网络模型，结合计算语言学与文学研究分析波斯诗人影响力动态，通过中心性指标和社区检测识别关键诗人和文学流派，为数字人文提供可扩展的诗歌传统分析框架。
摘要：
本研究形式化了一个计算模型，通过构建多维相似性网络来模拟古典波斯诗人的影响力动态。基于Ganjoor语料库精心整理的数据集，我们利用语义、词汇、风格、主题和韵律特征来界定每位诗人的语料库。每个特征都包含在加权相似性矩阵中，随后将这些矩阵拼接以生成展示诗人间相互影响的聚合图。通过计算度中心性、接近中心性、中介中心性、特征向量中心性和Katz中心性指标，我们进一步开展网络分析以识别关键诗人、风格枢纽和桥梁型诗人。此外，为获得类型学洞察，我们使用Louvain社区检测算法来划分具有风格和主题一致性的诗人集群，这些集群与Sabk-e Hindi、Sabk-e Khorasani和Bazgasht-e Adabi现象等公认文学流派高度吻合。我们的发现为波斯文学提供了新的数据驱动视角，区分了经典重要性与互文影响力，从而突显了具有重大结构意义的相对次要人物。结合计算语言学与文学研究，本文为诗歌传统构建了可解释且可扩展的模型，支持数字人文领域的回溯反思与前瞻研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
