Порівняння методів

	Author Co-Citation Analysis (ACA)	Direct Citation Clustering of Science
Галузь	Бібліометрія	Бібліометрія
Родина	Process / pipeline	Process / pipeline
Рік появи	1981	2010
Автор методу	Howard D. White & Belver C. Griffith; later Howard D. White & Katherine W. McCain	Kevin W. Boyack & Richard Klavans; Ludo Waltman & Nees Jan van Eck
Тип	Science-mapping pipeline using authors as units of analysis	Large-scale publication-level network clustering pipeline
Основоположне джерело	White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163-171.	Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404.
Інші назви	ACA, Author Co-Citation Mapping, Cited-Author Co-Citation Analysis	Direct Citation Network Clustering, Publication-Level Citation Clustering, Citation-Based Science Mapping
Пов'язані	3	3
Підсумок	Author co-citation analysis (ACA) maps the intellectual structure of a research field by treating authors, rather than documents, as the units of analysis. Introduced by Howard White and Belver Griffith in 1981, ACA rests on a simple premise: when two authors are repeatedly cited together in the same later papers, the community of citers is signaling that their work is intellectually related. By counting these co-citations across a body of literature, assembling them into an author-by-author matrix, converting that matrix into similarities, and projecting it into a low-dimensional map, ACA recovers the 'specialties' or schools of thought that organize a discipline and shows how they relate to one another. White and McCain's 1998 study of information science, which mapped 120 leading authors over more than two decades, became the canonical demonstration of the method and established its workflow.	Direct citation clustering maps the structure of science by linking publications through the citations that run directly between them and partitioning the resulting network into research areas. Unlike co-citation (which links papers cited together) or bibliographic coupling (which links papers sharing references), direct citation uses the citation itself as the edge: paper A is connected to paper B because A cites B. Kevin Boyack and Richard Klavans's 2010 comparison of citation approaches found that, at scale, direct citation can represent the research front at least as accurately as the alternatives, and Ludo Waltman and Nees Jan van Eck's 2012 methodology showed how to cluster very large direct-citation networks — millions of publications — into a coherent, publication-level classification of science using modularity-based community detection. Together these works established direct citation clustering as a leading technique for building large-scale science maps.