摘要
研究前沿探测是科学计量学的重要研究领域,而时间特征识别是对研究前沿新颖性进行判定的一个重要步骤。本文基于文献共被引分析方法分别用五种关联强度指标构建关联矩阵,进而通过Louvain算法获取研究前沿的聚类,从而比较不同关联强度指标对研究前沿新颖性的时间特征识别的灵敏度。通过对科学计量学领域的案例研究,我们发现五种关联强度指标对研究前沿新颖性的时间特征的识别产生了不同的影响,其中Inclusion指数在分析新颖性的时间特征上的最终结果中表现较好。
Detection of research fronts is an important field of research in scientometrics,and recognition of time characteristics is an important step of the newness feature in determining research fronts.Based on the co-citation analysis method,five kinds of association strength indicators are used to construct the incidence matrix.The Louvain algorithm is then used to obtain clusters of the research front fields,and the sensitivity of different association strength indicators to the time characteristics recognition of the research front is compared.Through the case studies in the field of scientometrics,we find that the five kinds of association strength indicators have different effects on the identification of the time characteristics of research fronts.The inclusion index performs better in analyzing the final results of time characteristics.
作者
黄福
侯海燕
胡志刚
Huang Fu;Hou Haiyan;Hu Zhigang(Science of Science and Management of Science of Technology Research Institute,Dalian University of Technology,Dalian 116024)
出处
《情报学报》
CSSCI
CSCD
北大核心
2018年第6期561-568,共8页
Journal of the China Society for Scientific and Technical Information
基金
国家社会科学基金项目"高科技前沿监测中的知识图谱方法与应用研究"(14BTQ030)
关键词
研究前沿
新颖性
时间特征
关联强度
相似性计算
research front
newness
time characteristics
association strength
similarity measures
