Сравнение на методи

	Анализ на мрежи от съавторство	Научно картографиране
Област	Библиометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2001	2000s
Създател	Mark E. J. Newman and others	Katy Börner, Chaomei Chen, and others
Тип	Method	Method
Основополагащ източник	Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.	Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.
Други названия	collaboration network, authorship network, research collaboration mapping	knowledge mapping, domain mapping, research landscape visualization
Свързани	4	5
Резюме	Co-authorship network analysis is a method that maps research collaboration patterns by treating authors as nodes and co-authored papers as edges in a network graph. The structure, density, and centrality patterns of this network reveal how researchers connect, collaborate across institutions and disciplines, and form research communities. Pioneered formally by Newman (2001), co-authorship analysis provides quantitative insights into the social fabric of science, revealing collaboration patterns, identifying scientific leaders, and detecting institutional or disciplinary boundaries.	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.