河南师范大学学报(自然科学版)

2022, v.50;No.226(05) 63-70

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知乎标签网络的演化及模型研究
Study on the formation mechanism of Zhihu tagging network

黄涛;王胜烽;吴晔;张鹏;肖井华;
Huang Tao;Wang Shengfeng;Wu Ye;Zhang Peng;Xiao Jinghua;School of Science,Beijing University of Posts and Telecommunications;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications;Computational Communication Research Center,Beijing Normal University;

摘要(Abstract):

知识网络是探索知识发展脉络和形成机制的重要基础.研究知识网络的统计特性和形成机理具有重要意义.标签网络作为知识网络的一种,近年来受到研究者的关注,但是目前关于标签网络生长机制的研究还较为缺乏.基于知乎问答平台的标签数据构建知识标签网络,统计分析了标签网络的静态统计特性以及演化特性.为了理解知识标签网络复杂结构的形成原因,提出了知识激发问题的网络动态生长模型,模型假定新问题由知识标签激发,知识标签激发问题的能力与其度值正相关.仿真结果表明,模型可以很好地再现知乎标签网络的统计特性和社团化结构.研究结果揭示了标签知识网络增长过程中表现的动态演化特性,并基于实证结果建立的标签网络生长模型,对理解知识网络的形成和发展有一定启发意义.
Knowledge networks are important for exploring knowledge development, thus it is important to study the statistical characteristics and formation mechanism of knowledge networks. A kind of knowledge network, tagging networks has received attention from researchers in recent years. However, there is still a lack of research on the growth mechanism of tagging networks. In this paper, we construct tagging networks based on the tagging data of Zhihu Q & A platform, and statistically analyze its static statistical characteristics and evolutionary characteristics. In order to understand the complex structure of the tagging network, we propose a formation model for the network, which assumes that new questions are inspired by knowledge tags and the ability of knowledge tags to inspire questions is positively related to their degree. The simulation results show that the model can well reproduce the statistical properties and the association structure of the tagging network. This paper reveals the dynamic evolutionary characteristics exhibited in the growth process of the tagging network. The formation model of the tagging network based on the empirical results is enlightening for understanding the formation of other knowledge networks.

关键词(KeyWords): 标签网络;问答社区;演化模型;幂律分布
tagging networks;question and answer community;evolutionary model;power law distribution

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划(2020YFF0305300)

作者(Authors): 黄涛;王胜烽;吴晔;张鹏;肖井华;
Huang Tao;Wang Shengfeng;Wu Ye;Zhang Peng;Xiao Jinghua;School of Science,Beijing University of Posts and Telecommunications;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications;Computational Communication Research Center,Beijing Normal University;

DOI: 10.16366/j.cnki.1000-2367.2022.05.009

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