Comparar métodos

	Percentile-Based Citation Impact (PPtop10%)	Eigenfactor and Article Influence Score
Campo	Bibliometría	Bibliometría
Familia	Process / pipeline	Process / pipeline
Año de origen	2011	2007
Autor original	Lutz Bornmann & Loet Leydesdorff; Ludo Waltman & Michael Schreiber	Carl T. Bergstrom; Jevin D. West, Theodore C. Bergstrom & Carl T. Bergstrom
Tipo	Distribution-based citation impact pipeline	Eigenvector-based journal ranking pipeline
Fuente seminal	Leydesdorff, L., & Bornmann, L. (2011). Integrated impact indicators compared with impact factors: An alternative research design with policy implications. Journal of the American Society for Information Science and Technology, 62(11), 2133-2146.	Bergstrom, C. T. (2007). Eigenfactor: Measuring the value and prestige of scholarly journals. College & Research Libraries News, 68(5), 314-316.
Alias	Percentile Rank Citation Indicators, Top 10% Highly Cited Papers Indicator, PPtop10%, Integrated Impact Indicator (I3)	Eigenfactor Score, Article Influence Score, Network-Weighted Journal Prestige, Eigenvector Journal Metrics
Relacionados	3	3
Resumen	Percentile-based citation impact replaces the average citation count with a paper's rank position within a properly defined reference set. Instead of asking how many citations a paper received, it asks where the paper falls in the citation distribution of comparable papers from the same field, year, and document type. Because citation distributions are extremely skewed, a single highly cited paper can inflate a mean, so Lutz Bornmann and Loet Leydesdorff argued that impact should be measured non-parametrically through percentile ranks and the share of papers reaching the top of their field. The most widely used summary is PPtop10%, the proportion of a unit's papers that belong to the most-cited 10% of their reference set; Leydesdorff and Bornmann's Integrated Impact Indicator (I3) generalizes this idea by integrating the full percentile curve. Ludo Waltman and Michael Schreiber clarified how percentile ranks should be computed when many papers share the same citation count.	The Eigenfactor Score and its per-article companion, the Article Influence Score, rank scholarly journals by treating the citation network as a system in which a citation from a prestigious journal counts for more than a citation from an obscure one. Carl Bergstrom introduced the Eigenfactor in 2007 using the same recursive idea behind Google's PageRank: a journal is important if it is cited by other important journals. The score is computed as the stationary distribution of a random walk over the journal-to-journal citation matrix, so it captures not just how often a journal is cited but where those citations come from. The Eigenfactor measures a journal's total influence and therefore scales with size; dividing by the journal's share of articles yields the Article Influence Score, a per-paper measure comparable to a normalized impact factor. West, Bergstrom and Bergstrom set out the full network methodology in 2010.