Сравнение методов

	Анализ ко-оoccurrences ключевых слов	Коцитатный анализ
Область	Библиометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s	1973
Автор метода	Bibliometric research community	Henry Small
Тип	Method	Method
Основополагающий источник	Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166.	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.
Другие названия	term co-occurrence, keyword network analysis, thematic analysis, term clustering	co-citation mapping, historiograph, direct citation, citation pair analysis
Связанные	4	5
Сводка	Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries.	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.