Сравнение на методи

	Анализ на съвместно цитиране на списания	Анализ на ко-окурентност на ключови думи
Област	Библиометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1981	2000s
Създател	Henry Small, Henry White, and others	Bibliometric research community
Тип	Method	Method
Основополагащ източник	White, H. D., & Griffith, B. C. (1981). Author co-citation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171.	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.
Други названия	journal citation mapping, journal network analysis, cited source co-citation	term co-occurrence, keyword network analysis, thematic analysis, term clustering
Свързани	4	4
Резюме	Journal co-citation analysis is a bibliometric method that maps the intellectual structure of a research field by analyzing how frequently pairs of journals are cited together in the same papers. Two journals are co-cited when papers cite both journals, indicating that the journals are perceived as intellectually related by the citing authors. This extension of paper-level co-citation analysis to the journal level reveals the topological structure of journal relationships, disciplinary boundaries, and the role of different journals within research communities.	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.