Eigenfactor and Article Influence Score
Also known as: Eigenfactor Score, Article Influence Score, Network-Weighted Journal Prestige, Eigenvector Journal Metrics
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.
Key highlights
- Weights each citation by the influence of the citing journal, so prestige propagates through the network rather than being counted flatly.
- Excludes journal self-citations by construction, removing a common avenue for gaming.
- Uses a five-year window suited to slower-citing fields and reduced volatility relative to the two-year impact factor.
- Provides both a total-influence measure (Eigenfactor) and a size-normalized per-article measure (Article Influence Score).
Intuition
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How it works
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When to use it
Use Eigenfactor metrics when you want a journal ranking that weights citations by the prestige of their source rather than counting them equally, and when a five-year, self-citation-free window suits the citing behavior of your field. The Eigenfactor Score is appropriate when you care about a journal's total influence on the literature, while the Article Influence Score is the measure to use when comparing journals of different sizes on a per-article basis, much as you would use a normalized impact factor. The approach is well suited to fields with rich, well-covered citation networks. It is less informative for very new journals with sparse incoming links, for fields poorly covered by the source database, and at the level of individual articles or authors, since it is fundamentally a journal-network measure. It complements, rather than replaces, distribution-based indicators that operate on individual papers.
Strengths & limitations
- Weights each citation by the influence of the citing journal, so prestige propagates through the network rather than being counted flatly.
- Excludes journal self-citations by construction, removing a common avenue for gaming.
- Uses a five-year window suited to slower-citing fields and reduced volatility relative to the two-year impact factor.
- Provides both a total-influence measure (Eigenfactor) and a size-normalized per-article measure (Article Influence Score).
- The Eigenfactor Score scales with journal size, so it cannot be compared directly across journals of different output without the per-article normalization.
- Results depend on the coverage of the source citation database and on the chosen window and damping factor.
- Like all journal-level metrics it should not be used to evaluate individual articles or researchers.
- Field differences in citation density and network structure still affect cross-discipline comparisons despite the network weighting.
Common pitfalls
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Applications
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Frequently asked
How is the Eigenfactor different from the impact factor?
The impact factor counts citations equally and averages them over two years of articles. The Eigenfactor weights each citation by the influence of the citing journal, computed as the stationary distribution of a random walk over the citation network, and uses a five-year window with self-citations removed. It therefore captures where citations come from, not just how many there are. The Eigenfactor measures total influence and scales with size, whereas the Article Influence Score divides by article share to give a per-paper measure directly comparable to a normalized impact factor.
Why are journal self-citations excluded?
Setting the diagonal of the citation matrix to zero prevents a journal from boosting its own score by citing itself, which is one of the easiest ways to manipulate a flat citation count. By removing self-citations before computing the random walk, the Eigenfactor ensures a journal's influence reflects how the rest of the literature values it. This makes the metric more robust to gaming than the standard impact factor, though it does not eliminate the possibility of coordinated cross-citation between cooperating journals.
When should I use the Article Influence Score instead of the Eigenfactor?
Use the Article Influence Score whenever you are comparing journals of different sizes, because the raw Eigenfactor scales with how many articles a journal publishes and a large journal will naturally accumulate more total influence. The Article Influence Score divides the Eigenfactor by the journal's share of all articles and rescales so the average journal scores one, giving a size-independent per-paper measure. The Eigenfactor itself is the right choice only when you genuinely want a journal's total influence on the literature.
Sources
- 1.Bergstrom, C. T. (2007). Eigenfactor: Measuring the value and prestige of scholarly journals. College & Research Libraries News, 68(5), 314-316.
- 2.West, J. D., Bergstrom, T. C., & Bergstrom, C. T. (2010). The Eigenfactor Metrics: A network approach to assessing scholarly journals. College & Research Libraries, 71(3), 236-244.
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Cite this page
ScholarGate. (2026, June 23). Eigenfactor and Article Influence Score. ScholarGate. https://scholargate.app/bibliometrics/eigenfactor-metrics