基于矩阵相似度的主题演化路径判别研究  

Identifying Research Topic Evolutionary Paths Based on Matrix Similarity

作　　者：黄菡 王晓光[1,2] 何静[1] 王宏宇 Huang Han;Wang Xiaoguang;He Jing;Wang Hongyu(School of Information Management,Wuhan University,Wuhan 430072;Big Data Institute,Wuhan University,Wuhan 430072;School of Management,Wuhan University of Technology,Wuhan 430070)

机构地区：[1]武汉大学信息管理学院,武汉430072 [2]武汉大学大数据研究院,武汉430072 [3]武汉理工大学管理学院,武汉430070

出　　处：《情报学报》2023年第11期1265-1275,共11页Journal of the China Society for Scientific and Technical Information

基　　金：国家自然科学基金面上项目“基于大规模开放科学知识图谱的学科新兴趋势探测研究”(71874129)。

摘　　要：研究主题演化对于厘清科学发展脉络和预测前沿趋势意义重大。对前后时段主题间的相似度进行计算以识别演化路径是研究主题演化的核心步骤。本文创新性地提出了基于矩阵相似度的主题演化路径判别方法。该方法以共词网络中研究主题的网络结构为基础,在主题相似度计算时,考虑了研究主题在词及词间关系两个方面的相似性。在此基础上,本文构建了一套基于矩阵相似度的研究主题演化分析框架。通过引入分段线性表示法对数据进行时段划分以构建时序共词网络,在利用社区发现算法识别各时段共词网络中的主题社区后,通过计算主题新颖度、流行度、核心度、成熟度等多维度特征指标来表征研究主题类型,进一步通过矩阵相似度关联前后时段的研究主题以识别研究主题演化路径,最终通过桑基图和多维战略坐标图等形式对主题演化过程进行可视化。本文以图书情报领域为例开展实证分析,研究结果表明,本文方法能有效支撑学科领域内的研究主题演化分析,为辅助科研决策提供方法论支持。The evolution of research topics is critical for clarifying scientific development and predicting frontiers.The calculation of the similarity of topics in adjacent time periods and identification of their evolutionary paths is the core step in the topic evolution analysis.In this study,the matrix similarity algorithm is innovatively proposed to identify the topic evolution path.Based on the local network structure of the research topic in the co-word network,the similarity of the research topic in terms of words and relations is considered in the calculation of topic similarity.Subsequently,an analytical framework for research topic evolution based on matrix similarity is constructed.Considering piecewise linear representation,the framework divides the data into time periods to build the temporal co-word networks.After identifying the topic communities in the co-word network at each time period using the community discovery algorithm,multi-dimensional feature indexes such as novelty,popularity,core,and maturity are calculated to represent the types of research topics.The evolutionary paths of the research topics are then identified using matrix similarity calculations.Finally,the evolution process of the research topics is visualized by a Sankey diagram and a multi-dimensional strategic coordinate plot.Specifically,this study uses the field of library and information science as an example of empirical analysis.The results show that the proposed method can effectively support the evolution analysis of research topics in a research area and provide methodological support for research decision-making.

关 键 词：研究主题演化 矩阵相似度 共词网络 分段线性表示 多维指标 

分 类 号：G250[文化科学—图书馆学] TP391.1[自动化与计算机技术—计算机应用技术]