hg-Index (Composite Hirsch-Egghe)
Also known as: Alonso hg-index, hg index, composite h-g index
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.
Key highlights
- Blends the robustness of the h-index with the citation sensitivity of the g-index in one composite number.
- Uses a geometric mean that leans toward the conservative h-index, tempering the g-index's vulnerability to a single outlier.
- Always lies between h and g, giving a bounded, interpretable value on the same scale as its components.
- More discriminating than the h-index, useful for separating researchers with equal h but different g.
Intuition
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How it works
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When to use it
Use the hg-index when you want a single author-level number that combines the robustness of the h-index with the citation sensitivity of the g-index, particularly when comparing researchers whose h-indices are similar but whose top-paper impact differs. It is useful as a tie-breaker and as a more discriminating summary than the h-index alone, while being less vulnerable to a lone outlier than the g-index. It is computed from the same data as h and g and adds negligible cost. It is not field-normalized and should not be used for cross-disciplinary ranking, and like its components it depends on a clean publication record and is best reported alongside h and g rather than instead of them.
Strengths & limitations
- Blends the robustness of the h-index with the citation sensitivity of the g-index in one composite number.
- Uses a geometric mean that leans toward the conservative h-index, tempering the g-index's vulnerability to a single outlier.
- Always lies between h and g, giving a bounded, interpretable value on the same scale as its components.
- More discriminating than the h-index, useful for separating researchers with equal h but different g.
- Adds little new information beyond h and g, since it is a deterministic function of the two.
- Not field-normalized, so it cannot be compared fairly across disciplines.
- Inherits both components' dependence on accurate, deduplicated author publication and citation data.
- Still influenced, if more gently, by the g-index's sensitivity to extreme papers, since g is one of its inputs.
Common pitfalls
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Applications
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Frequently asked
How is the hg-index computed?
Compute the author's h-index and Egghe's g-index from the same ranked citation list, then take the geometric mean of the two, the square root of h times g. The result is a single composite that combines the robustness of h with the citation sensitivity of g and always lies between the two values.
Why use a geometric mean rather than an average of h and g?
Alonso and colleagues chose the geometric mean because it is naturally pulled toward the smaller of the two numbers, which is the conservative h-index. This dampens the effect of an inflated g caused by a single extraordinarily cited paper, so the composite is more robust than a plain arithmetic average would be while still responding to genuine top-paper impact. The geometric mean also keeps the result on the same numerical scale as h and g.
Does the hg-index add information beyond h and g?
Not strictly, since it is a deterministic function of the two. Its value is as a convenient, more discriminating single summary that balances the components and serves well as a tie-breaker between authors with equal h. For transparency it should be reported alongside h and g rather than replacing them, and like all these indices it is not field-normalized and should be used within, not across, disciplines.
Sources
- 1.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.
- 2.Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152.
- 3.Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572.
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Cite this page
ScholarGate. (2026, June 23). hg-Index (Composite Hirsch-Egghe). ScholarGate. https://scholargate.app/bibliometrics/hg-index