Compare methods

	Garfield's Law of Concentration	Collaboration Distance and Erdős Number Analysis
Field	Bibliometrics	Bibliometrics
Family	Process / pipeline	Process / pipeline
Year of origin	1972	2001
Originator	Eugene Garfield	M. E. J. Newman (collaboration networks); Rodrigo de Castro & Jerrold Grossman (Erdős number)
Type	Descriptive bibliometric law and core-journal identification pipeline	Network-distance pipeline over co-authorship graphs
Seminal source	Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471-479.	Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404-409.
Aliases	Law of Concentration, Core Journal Concentration, Garfield Concentration Law	Erdős Number Analysis, Co-Authorship Distance, Collaboration Geodesic Analysis, Scientific Small-World Analysis
Related	3	3
Summary	Garfield's Law of Concentration is the bibliometric principle that the bulk of the significant scientific literature is concentrated in a relatively small, largely multidisciplinary core of journals. Formulated by Eugene Garfield — founder of the Science Citation Index — and presented in his 1972 Science paper 'Citation analysis as a tool in journal evaluation', the law observes that when journals are ranked by how often they are cited, a core of perhaps 500 to 1,000 journals accounts for the overwhelming majority of all citations across all fields. Garfield framed it as a generalization and corollary of Bradford's earlier law of scattering: the long tail of any one discipline's literature is in large part composed of the cores of other disciplines, so a single multidisciplinary core covers science as a whole. The law underpins the rationale for selective, citation-based journal indexing.	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.