方法对比

	文献耦合分析	共被引分析	科学制图
领域	文献计量学	文献计量学	文献计量学
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1963	1973	2000s
提出者	Melvin M. Kessler	Henry Small	Katy Börner, Chaomei Chen, and others
类型	Method	Method	Method
开创性文献	Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(3), 123–131.	Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.	Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.
别名	document coupling, bibliographic similarity	co-citation mapping, historiograph, direct citation, citation pair analysis	knowledge mapping, domain mapping, research landscape visualization
相关	5	5	5
摘要	Bibliographic coupling is a method that identifies intellectual relationships between documents by measuring their shared references. Two papers are considered 'coupled' when they cite the same sources, indicating they address related research questions or draw from the same conceptual foundations. Introduced by Kessler in 1963, this approach enables researchers to map knowledge domains and discover thematically similar publications without relying on subject cataloging or keywords.	Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time.	Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning.