Сравнение методов

	Scientific Collaboration Index (Co-Authorship Intensity)	Collaboration Distance and Erdős Number Analysis
Область	Библиометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline
Год появления	1983	2001
Автор метода	K. Subramanyam (review and Collaboration Index); S. M. Lawani (collaborative measures)	M. E. J. Newman (collaboration networks); Rodrigo de Castro & Jerrold Grossman (Erdős number)
Тип	Descriptive bibliometric indicators of co-authorship intensity	Network-distance pipeline over co-authorship graphs
Основополагающий источник	Subramanyam, K. (1983). Bibliometric studies of research collaboration: A review. Journal of Information Science, 6(1), 33-38.	Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404-409.
Другие названия	Degree of Collaboration, Collaborative Coefficient, Co-Authorship Intensity, Collaboration Index	Erdős Number Analysis, Co-Authorship Distance, Collaboration Geodesic Analysis, Scientific Small-World Analysis
Связанные	3	3
Сводка	The Scientific Collaboration Index family quantifies how collaborative a body of research is by analyzing the number of authors per paper. In his influential 1983 review of bibliometric studies of research collaboration, K. Subramanyam consolidated the main measures: the Degree of Collaboration (the proportion of multi-authored papers), the Collaboration Index (the mean number of authors per paper), and related indicators. S. M. Lawani and later Ajiferuke and colleagues refined these into the Collaborative Coefficient, which weights papers by how many authors share them while keeping the index bounded. Together these indices give simple, comparable summaries of co-authorship intensity that have documented the long-term rise of team science across nearly every field, and they remain standard descriptive tools in scientometrics, library science, and research-policy studies of collaboration.	Collaboration distance analysis measures how closely connected scientists are through chains of co-authorship. Two researchers who have written a paper together are at distance 1; if they share a co-author but never wrote together, distance 2; and so on. The most famous instance is the Erdős number, the collaboration distance to the prolific mathematician Paul Erdős, popularized by the Erdős Number Project and analyzed by Rodrigo de Castro and Jerrold Grossman. M. E. J. Newman's landmark 2001 PNAS study generalized this idea, constructing large co-authorship networks across physics, biomedicine, and computer science and showing that they are 'small worlds': despite millions of authors, typical shortest paths are short and local clustering is high. Collaboration distance analysis thus characterizes the connectivity and reach of scientific communities through the geometry of their co-authorship graphs.