Analysis of the Social Network and the Evolution of the Influence of Ancient Chinese Poets

Abstract

Chinese classical poetry occupies an important position in ancient Chinese literature. However, the existing research on Chinese classical poetry is usually limited to a certain poet or dynasty to analyze its historical and cultural influence and lacks comprehensive research on the process of ancient poetry from the perspective of time and space. We integrate multisource data and information, including poets’ biographies and Chinese classical poetry, to build a relatively complete social network of 41,310 poets. Based on this network, we use natural language processing and social network analysis techniques to research the relationships between poets. For example, how poets of different dynasties and different schools relate to each other. In order to quantitatively analyze the changing process of poets’ influence over a period of time, we propose a new method—time-series entropy weight method—to calculate the dynamic changing process of poets’ influence over time. Besides, we evaluate and discuss the method of calculating the influence of poets by means of propagation dynamics model and verify the effectiveness of the proposed method. Through the study of the complex social relations of poets and the quantification of their influence, we can assist in the study of the development of different schools and styles of ancient poetry. Our work offers a new, data-driven, long-run perspective on the evolution of Chinese poetry for historical researchers and enthusiasts to understand the complex relationships among historical figures.

Keywords

Chinese classical poetry complex network social network analysis node importance entropy weighting method

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