เปรียบเทียบวิธี

	Eigenfactor and Article Influence Score	Journal Self-Citation Analysis
สาขาวิชา	บรรณมิติ	บรรณมิติ
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2007	1999
ผู้ริเริ่ม	Carl T. Bergstrom; Jevin D. West, Theodore C. Bergstrom & Carl T. Bergstrom	Wolfgang Glanzel et al.; Ronald Rousseau
ประเภท	Eigenvector-based journal ranking pipeline	Self-citation decomposition pipeline
แหล่งต้นตำรับ	Bergstrom, C. T. (2007). Eigenfactor: Measuring the value and prestige of scholarly journals. College & Research Libraries News, 68(5), 314-316.	Glanzel, W., Debackere, K., Thijs, B., & Schubert, A. (2006). A concise review on the role of author self-citations in information science, bibliometrics and science policy. Scientometrics, 67(2), 263-277.
ชื่อเรียกอื่น	Eigenfactor Score, Article Influence Score, Network-Weighted Journal Prestige, Eigenvector Journal Metrics	Self-Citation Rate Analysis, Journal Self-Referencing Analysis, Self-Citing and Self-Cited Rates, Citation Manipulation Detection
ที่เกี่ยวข้อง	3	3
สรุป	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.	Journal self-citation analysis separates the citations a journal gives to itself from the citations it gives to and receives from the wider literature, in order to understand a journal's internal coherence and to detect potential inflation of impact metrics. Ronald Rousseau showed in 1999 that a journal's citation curve is really two curves superimposed: a self-citation component and an external-citation component, each with its own timing. Wolfgang Glänzel and colleagues, surveying the self-citation literature, distinguished the legitimate, communicative role of self-citation from its problematic use to manipulate indicators, and clarified how to measure its effect. The analysis revolves around two complementary rates: the self-cited rate, the share of a journal's incoming citations that come from itself, and the self-citing rate, the share of its outgoing references that point to itself. Comparing impact metrics with and without self-citations reveals how much a journal's standing depends on citing itself.