| 基于无标度与分形理论的层次知识网络原理解析 |
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| 引用本文： | 滕广青,白淑春,韩尚轩,毕强.基于无标度与分形理论的层次知识网络原理解析[J].图书情报工作,2017,61(14):132-140. |
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| 作者姓名： | 滕广青 白淑春 韩尚轩 毕强 |
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| 作者单位： | 1. 东北师范大学信息科学与技术学院 长春 130117;
2. 吉林大学图书馆 长春 130012;
3. 吉林大学管理学院 长春 130022 |
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| 基金项目： | 本文系国家自然科学基金项目"基于网络结构演化的Folksonomy模式中社群知识组织与知识涌现研究"（项目编号：71473035）研究成果之一。 |
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| 摘 要： | 目的/意义]在保障知识网络的整体性征的条件下,从原始知识网络中提取具有显著意义的层次知识网络,奠定基于关联频度提取层次知识网络的理论基础。方法/过程]以无标度网络与分形几何作为基础理论支撑,在对关键词知识网络和标签知识网络关联频度分布进行分析的基础上,采用关联频度作为阈值提取层次知识网络。并对层次知识网络的无标度性和小世界效应两项网络整体性征进行验证。结果/结论]知识网络的关联频度分布服从幂律分布。以关联频度为阈值提取的层次知识网络在节点度值分布和关联频度分布方面都保持了原始整体网络的无标度性。层次知识网络能够很好地保持原始网络所具有的小世界特征。基于关联频度提取的层次知识网络与原始知识网络等效。
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| 关 键 词： | 知识网络 幂律 无标度 分形 层次网络 |
| 收稿时间： | 2017-04-25 |
Analysis on the Principle of Knowledge Network at Level Based on Scale-free and Fractal Theory |
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| Teng Guangqing,Bai Shuchun,Han Shangxuan,Bi Qiang.Analysis on the Principle of Knowledge Network at Level Based on Scale-free and Fractal Theory[J].Library and Information Service,2017,61(14):132-140. |
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| Authors: | Teng Guangqing Bai Shuchun Han Shangxuan Bi Qiang |
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| Institution: | 1. School of Information Science and Technology, Northeast Normal University, Changchun 130117;
2. Library of Jilin University, Changchun 130012;
3. School of Management, Jilin University, Changchun 130022 |
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| Abstract: | Purpose/significance] Under the condition of guaranteeing the whole character of knowledge networks, this paper aims to extract a significant knowledge networks at level from the original knowledge networks, in order to establish the theoretical basis of the knowledge network at level extracted by correlation frequency. Method/process] Under the basic theory support of scale-free network and fractal geometry, based on the analysis on the correlation frequency distribution of keywords knowledge network and tags knowledge network, this article extracts the knowledge networks at level using the correlation frequency as the threshold. The scale-free and small-world effect of the knowledge networks at level are verified. Result/conclusion] The correlation frequency distributions of the knowledge networks match power-law distribution. The knowledge networks at level, which take the correlation frequency as the threshold, keep the scale-free of whole original networks at nodes degree distribution and correlation frequency distribution. The knowledge networks at level can keep the small-world characteristics of the original network well. The knowledge network at level based on correlation frequency is equivalent to the original knowledge network. |
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| Keywords: | knowledge network power-law scale-free fractal network at level |
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