Compare methods

	e-Index (Excess Citations)	g-Index (Egghe)	hg-Index (Composite Hirsch-Egghe)	i10-Index
Field	Bibliometrics	Bibliometrics	Bibliometrics	Bibliometrics
Family	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Year of origin	2009	2006	2010	2011
Originator	Chun-Ting Zhang	Leo Egghe	Sergio Alonso, Francisco J. Cabrerizo, Enrique Herrera-Viedma & Francisco Herrera	Google Scholar (Google Scholar Citations / My Citations)
Type	Author-level excess-citation impact index	Author-level cumulative-citation impact index	Composite author impact index	Author-level productivity count of well-cited papers
Seminal source	Zhang, C.-T. (2009). The e-index, complementing the h-index for excess citations. PLoS ONE, 4(5), e5429.	Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152.	Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., & Herrera, F. (2010). hg-index: a new index to characterize the scientific output of researchers based on the h- and g-indices. Scientometrics, 82(2), 391-400.	Cornell University Library. Measuring Your Research Impact: i10-Index. Defines the i10-index as the number of publications with at least 10 citations, created and used by Google Scholar.
Aliases	Zhang e-index, excess citation index, e index	Egghe g-index, Egghe index, g index	Alonso hg-index, hg index, composite h-g index	Google Scholar i10-index, i10 index, ten-citation index
Related	3	3	3	3
Summary	The e-index, proposed by Chun-Ting Zhang in 2009, isolates the citations that the h-index throws away. Inside the h-core of an author's h most-cited papers, the h-index implicitly credits each paper with only h citations and discards everything above that, even though top papers may have far more. The e-index recovers exactly this surplus: it is the square root of the difference between the total citations of the h-core and the h-squared citations that the h-index already accounts for. Zhang designed it as a complement rather than a replacement for the h-index, so that the pair (h, e) together describe both the size of an author's productive core and the concentration of excess impact within it.	The g-index, introduced by Leo Egghe in 2006, is an author-level bibliometric indicator designed to repair a structural weakness of Hirsch's h-index: its insensitivity to the size of the most-cited papers. Where the h-index caps the credit any single paper can earn at h, the g-index lets exceptionally cited articles raise an author's score. It is defined as the largest number g such that the g most-cited papers together accumulate at least g-squared citations. Because it rests on cumulative rather than per-paper citation counts, the g-index always equals or exceeds the h-index and rewards researchers whose impact is concentrated in a few landmark works as well as those with broad, steady output.	The hg-index, proposed by Alonso, Cabrerizo, Herrera-Viedma, and Herrera in 2010, fuses the two best-known author metrics into a single composite. The h-index is robust but ignores how heavily an author's top papers are cited, while Egghe's g-index rewards those highly cited papers but can be swayed by a single outlier. The hg-index takes the geometric mean of the two, producing a value that lies between them and inherits a balance of their strengths: it remains close to the stable h-index while still responding to the citation impact captured by g. The authors showed that the geometric mean stays nearer to the smaller, more conservative h-index than the larger g-index, tempering the latter's sensitivity to extreme papers.	The i10-index is a deliberately simple author-level metric introduced by Google Scholar in 2011 for its Scholar Citations profiles. It counts the number of a researcher's publications that have each accumulated at least ten citations. Unlike the h-index, whose threshold depends on the rank of the paper, the i10-index applies a single fixed cutoff, making it transparent and trivial to compute. Its appeal lies in this simplicity and in its native availability on every Google Scholar profile, though it is used almost exclusively within the Google Scholar ecosystem and offers less discriminating power than rank-based indices.