Author Co-Citation Analysis (ACA)
Also known as: ACA, Author Co-Citation Mapping, Cited-Author Co-Citation Analysis
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.
Key highlights
- Recovers the intellectual structure of a field directly from citing behavior, requiring no prior classification by the analyst.
- Operates at the level of authors and schools of thought, giving an interpretable, person-centered map of a discipline.
- Has a well-established, reproducible workflow (co-citation counts, correlation similarity, MDS, clustering) validated across many fields.
- Excellent for retrospective intellectual histories and literature reviews because it summarizes a large body of work into a readable map.
Intuition
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How it works
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When to use it
Use author co-citation analysis when you want a high-level map of the intellectual structure of a field or specialty and you are interested in influential authors and schools of thought rather than individual documents or current research fronts. ACA is well suited to retrospective, discipline-level studies — characterizing how a field is organized, who its central figures are, and how its specialties relate — and works best when there is a substantial, well-indexed citation record over a meaningful time span. It is less appropriate for very recent or fast-moving topics (co-citation accumulates slowly and emphasizes established, highly cited work), for tracking the emerging edge of research (where bibliographic coupling or direct citation is preferable), or where author-name ambiguity and the use of first-author-only citation indexing would seriously distort the counts. ACA characterizes a field's accumulated intellectual base, not its frontier.
Strengths & limitations
- Recovers the intellectual structure of a field directly from citing behavior, requiring no prior classification by the analyst.
- Operates at the level of authors and schools of thought, giving an interpretable, person-centered map of a discipline.
- Has a well-established, reproducible workflow (co-citation counts, correlation similarity, MDS, clustering) validated across many fields.
- Excellent for retrospective intellectual histories and literature reviews because it summarizes a large body of work into a readable map.
- Co-citation accumulates slowly and favors older, highly cited work, so ACA lags the research front and underrepresents recent contributions.
- Traditional citation indexing counts only first authors, biasing the analysis against frequent co-authors and collaborative fields.
- Author-name homonymy and synonymy (different authors sharing names, or one author cited under variants) distort co-citation counts unless disambiguated.
- Results depend heavily on the choice of source journals, time window, and citation threshold used to select the author set.
Common pitfalls
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Applications
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Frequently asked
How does author co-citation analysis differ from document co-citation analysis?
Document co-citation analysis, introduced by Henry Small, counts how often pairs of papers are cited together and maps documents as nodes. Author co-citation analysis, introduced by White and Griffith in 1981, raises the unit to authors: it counts how often pairs of authors are cited together across the literature and maps authors as nodes. Because an author's whole oeuvre is aggregated, ACA produces a more compact, person-centered picture of a field's schools of thought, whereas document co-citation gives finer-grained but more fragmented detail about specific influential works.
Why use Pearson correlations instead of raw co-citation counts?
White and McCain popularized converting raw co-citation counts into Pearson correlations between authors' co-citation profiles. The idea is that two authors are similar if they are co-cited with the same third authors in similar proportions, which captures overall structural position rather than just sheer joint frequency. Correlations also dampen the dominance of very highly cited authors and yield a matrix well behaved for multidimensional scaling. The choice has been debated — some argue raw or cosine measures better preserve the data — but profile correlation remains the classic ACA similarity.
Why does ACA lag the research front?
Co-citation requires that later authors cite two earlier authors together, and it takes time for such joint citations to accumulate. As a result, the map is dominated by established, heavily cited authors and reflects a field's accumulated intellectual base rather than its newest, fastest-moving topics. To study the emerging edge of research, analysts turn to bibliographic coupling or direct-citation methods, which link current publications through their shared references and so respond more quickly to new work.
Sources
- 1.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.
- 2.White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of the American Society for Information Science, 49(4), 327-355.
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Cite this page
ScholarGate. (2026, June 23). Author Co-Citation Analysis (ACA). ScholarGate. https://scholargate.app/bibliometrics/author-co-citation-analysis