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Eigenfactor, Article Influence Scores

In 2007, the Web of Science based JCR included Eigenfactor and Article Influence Scores, two
relatively complex metrics compared with JIF. These metrics were developed by Bergston Lab at
Washington State University and are freely accessible at Eigenfactor.org. The Eigenfactor includes the
influence of a journal based on whether it is cited within other reputable journals. A citation from a
highly-cited journal is worth more than from a journal with few citations. Similarly, it incorporates the
different standards for citation and different time scales on which citations occur for different subjects.
The Eigenfactor Score calculation is based on the number of times articles from the journal published
in the past five years have been cited in the JCR data year, but it also considers which journals have
contributed these citations so that highly cited journals will influence the network more than lesser
cited journals. In other words, if a journal receives citations from high-ranking or highly reputed
journals, Eigenfactor Score will be higher than another journal that receives most citations from
average-ranking journals. Citations from one article in a journal to another article from the same
journal are removed, so that Eigenfactor scores are not influenced by journal self-citation. To adjust
for different areas of research , the citations are also weighted by the length of the reference list that
they’re from. Simply stated, the Eigenfactor calculation is the number of weighted citations in the JCR
data year to articles published in a journal in the preceding five years, divided by the total number of
articles published in the journal within the same five year period.

Eigenfactor score is calculated using data from WoS and JCR. The Eigenfactor approach is considered
as more robust than the JIF metric, which purely counts incoming citations without considering the
significance of those citations. The Eigenfactor is a measure of the journal’s overall importance
to the research community. The score is a measure of a journal’s importance and it can be used in
combination with h-index to evaluate the work of individual scientists. Eigenfactors also tend to be
very small numbers as scores now are scaled so that the sum of all journal Eigenfactors in the JCR
adds up to 100, referred to as Normalized Eigenfactor, which rescales the Eigenfactor score so that
the average journal has a score of 1. Journals can then be compared and measured by their score
relative to 1. A journal with a Normalized Eigenfactor Score of 3 suggests that it is three times as good
the average journal in the JCR. This score does not take the journal size into account, implying that
larger journals tend to have larger Eigenfactors as they receive more citations overall.

Related to the Eigenfactor score, Article Influence (AI) score of a journal is a measure of the relative
importance of each of its articles over the first five years after publication. For example, if an article
published in 2010, AI measures average influence it made during 2011-2015. This is similar in its
interpretation to JCR’s Impact Factor. It is calculated by multiplying the Eigenfactor Score by
0.01 and dividing by the number of articles in the journal, which are normalized as a fraction of
all articles in all publications in a given database. The scores are normalized so that the mean article
in the entire JCR database has an article influence score of 1.00. A score greater than 1.00 suggests that
each article in the journal has above-average influence. One can find the methods of calculation of
EigenFactor and Article influence scores at eigenfactor.org.

Source Normalized Impact per Paper (SNIP)

SNIP is a key indicator as presented by CWTS Journal Indicators. Henk Moed developed the original
version in 2009, which was revised in 2012. Based on the Scopus database, SNIP attempts to measure
contextual citation impact by weighing citations based on the total number of citations in a subject
field and corrects subject-specific characteristics, simplifying cross-discipline comparisons between

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