Сравнение методов

	Библиометрические законы: законы Лотки, Брэдфорда и Ципфа	Анализ библиографических связей	Коцитатный анализ	Картографирование науки (Science Mapping)
Область	Библиометрия	Библиометрия	Библиометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1926–1949	1963	1973	2000s
Автор метода	Alfred J. Lotka, Samuel C. Bradford, George K. Zipf	Melvin M. Kessler	Henry Small	Katy Börner, Chaomei Chen, and others
Тип	Concept	Method	Method	Method
Основополагающий источник	Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.	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.
Другие названия	bibliometric distributions, productivity laws, frequency laws, information science laws	document coupling, bibliographic similarity	co-citation mapping, historiograph, direct citation, citation pair analysis	knowledge mapping, domain mapping, research landscape visualization
Связанные	3	5	5	5
Сводка	Three foundational empirical laws describe the structure and distribution of scientific information: Lotka's Law characterizes author productivity (most authors publish few papers; a few publish many), Bradford's Law describes journal concentration (a small number of core journals contain the majority of papers on a topic), and Zipf's Law models word and term frequency (word frequency inversely proportional to its rank). These regularities, discovered in the mid-20th century, are remarkably robust across disciplines and have become essential tools for understanding research productivity, organizing information resources, and designing search strategies.	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.