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  • × author_ss:"Egghe, L."
  1. Egghe, L.: Empirical and combinatorial study of country occurrences in multi-authored papers (2006) 0.02 
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    Abstract
    Papers written by several authors can be classified according to the countries of the author affiliations. The empirical part of this paper consists of two datasets. One dataset consists of 1,035 papers retrieved via the search "pedagog*" in the years 2004 and 2005 (up to October) in Academic Search Elite which is a case where phi(m) = the number of papers with m =1, 2,3 ... authors is decreasing, hence most of the papers have a low number of authors. Here we find that #, m = the number of times a country occurs j times in a m-authored paper, j =1, ..., m-1 is decreasing and that # m, m is much higher than all the other #j, m values. The other dataset consists of 3,271 papers retrieved via the search "enzyme" in the year 2005 (up to October) in the same database which is a case of a non-decreasing phi(m): most papers have 3 or 4 authors and we even find many papers with a much higher number of authors. In this case we show again that # m, m is much higher than the other #j, m values but that #j, m is not decreasing anymore in j =1, ..., m-1, although #1, m is (apart from # m, m) the largest number amongst the #j,m. The combinatorial part gives a proof of the fact that #j,m decreases for j = 1, m-1, supposing that all cases are equally possible. This shows that the first dataset is more conform with this model than the second dataset. Explanations for these findings are given. From the data we also find the (we think: new) distribution of number of papers with n =1, 2,3,... countries (i.e. where there are n different countries involved amongst the m (a n) authors of a paper): a fast decreasing function e.g. as a power law with a very large Lotka exponent.
    Source
    Information - Wissenschaft und Praxis. 57(2006) H.8, S.427-432
  2. Egghe, L.: Relations between the continuous and the discrete Lotka power function (2005) 0.02 
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    Abstract
    The discrete Lotka power function describes the number of sources (e.g., authors) with n = 1, 2, 3, ... items (e.g., publications). As in econometrics, informetrics theory requires functions of a continuous variable j, replacing the discrete variable n. Now j represents item densities instead of number of items. The continuous Lotka power function describes the density of sources with item density j. The discrete Lotka function one obtains from data, obtained empirically; the continuous Lotka function is the one needed when one wants to apply Lotkaian informetrics, i.e., to determine properties that can be derived from the (continuous) model. It is, hence, important to know the relations between the two models. We show that the exponents of the discrete Lotka function (if not too high, i.e., within limits encountered in practice) and of the continuous Lotka function are approximately the same. This is important to know in applying theoretical results (from the continuous model), derived from practical data.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.7, S.664-668
  3. Egghe, L.; Guns, R.; Rousseau, R.; Leuven, K.U.: Erratum (2012) 0.01 
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    Date
    14. 2.2012 12:53:22
    Footnote
    This article corrects: Thoughts on uncitedness: Nobel laureates and Fields medalists as case studies in: JASIST 62(2011) no,8, S.1637-1644.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.429
  4. Egghe, L.: Influence of adding or deleting items and sources on the h-index (2010) 0.01 
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    Abstract
    Adding or deleting items such as self-citations has an influence on the h-index of an author. This influence will be proved mathematically in this article. We hereby prove the experimental finding in E. Gianoli and M.A. Molina-Montenegro ([2009]) that the influence of adding or deleting self-citations on the h-index is greater for low values of the h-index. Why this is logical also is shown by a simple theoretical example. Adding or deleting sources such as adding or deleting minor contributions of an author also has an influence on the h-index of this author; this influence is modeled in this article. This model explains some practical examples found in X. Hu, R. Rousseau, and J. Chen (in press).
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.370-373
  5. Egghe, L.; Rousseau, R.: Averaging and globalising quotients of informetric and scientometric data (1996) 0.01 
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    Source
    Journal of information science. 22(1996) no.3, S.165-170
  6. Egghe, L.: ¬A universal method of information retrieval evaluation : the "missing" link M and the universal IR surface (2004) 0.01 
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    Date
    14. 8.2004 19:17:22
    Source
    Information processing and management. 40(2004) no.1, S.21-30
  7. Egghe, L.: Little science, big science and beyond (1994) 0.00 
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    Source
    Scientometrics. 30(1994) nos.2/3, S.389-392
  8. Egghe, L.: Expansion of the field of informetrics : the second special issue (2006) 0.00 
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    Source
    Information processing and management. 42(2006) no.6, S.1405-1407
  9. Egghe, L.: Expansion of the field of informetrics : origins and consequences (2005) 0.00 
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    Source
    Information processing and management. 41(2005) no.6, S.1311-1316
  10. Egghe, L.: Special features of the author - publication relationship and a new explanation of Lotka's law based on convolution theory (1994) 0.00 
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    Source
    Journal of the American Society for Information Science. 45(1994) no.6, S.422-427
  11. Egghe, L.: Note on a possible decomposition of the h-Index (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.871
  12. Egghe, L.: ¬The Hirsch index and related impact measures (2010) 0.00 
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    Source
    Annual review of information science and technology. 44(2010) no.1, S.65-114
  13. Egghe, L.: Informetric explanation of some Leiden Ranking graphs (2014) 0.00 
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    Abstract
    The S-shaped functional relation between the mean citation score and the proportion of top 10% publications for the 500 Leiden Ranking universities is explained using results of the shifted Lotka function. Also the concave or convex relation between the proportion of top 100?% publications, for different fractions ?, is explained using the obtained new informetric model.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.4, S.737-741
  14. Egghe, L.: ¬A good normalized impact and concentration measure (2014) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.10, S.2052-2054
  15. Egghe, L.; Rousseau, R.; Rousseau, S.: TOP-curves (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.777-785
  16. Egghe, L.: ¬A rationale for the Hirsch-index rank-order distribution and a comparison with the impact factor rank-order distribution (2009) 0.00 
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    Abstract
    We present a rationale for the Hirsch-index rank-order distribution and prove that it is a power law (hence a straight line in the log-log scale). This is confirmed by experimental data of Pyykkö and by data produced in this article on 206 mathematics journals. This distribution is of a completely different nature than the impact factor (IF) rank-order distribution which (as proved in a previous article) is S-shaped. This is also confirmed by our example. Only in the log-log scale of the h-index distribution do we notice a concave deviation of the straight line for higher ranks. This phenomenon is discussed.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2142-2144
  17. Egghe, L.; Ravichandra Rao, I.K.: ¬The influence of the broadness of a query of a topic on its h-index : models and examples of the h-index of n-grams (2008) 0.00 
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    Abstract
    The article studies the influence of the query formulation of a topic on its h-index. In order to generate pure random sets of documents, we used N-grams (N variable) to measure this influence: strings of zeros, truncated at the end. The used databases are WoS and Scopus. The formula h=T**1/alpha, proved in Egghe and Rousseau (2006) where T is the number of retrieved documents and is Lotka's exponent, is confirmed being a concavely increasing function of T. We also give a formula for the relation between h and N the length of the N-gram: h=D10**(-N/alpha) where D is a constant, a convexly decreasing function, which is found in our experiments. Nonlinear regression on h=T**1/alpha gives an estimation of , which can then be used to estimate the h-index of the entire database (Web of Science [WoS] and Scopus): h=S**1/alpha, , where S is the total number of documents in the database.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.10, S.1688-1693
  18. Egghe, L.; Guns, R.; Rousseau, R.: Thoughts on uncitedness : Nobel laureates and Fields medalists as case studies (2011) 0.00 
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    Footnote
    Vgl.: Erratum. In: Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.429.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.8, S.1637-1644
  19. Egghe, L.: ¬The amount of actions needed for shelving and reshelving (1996) 0.00 
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    Source
    Library management. 17(1996) no.1, S.18-24
  20. Egghe, L.; Rousseau, R.: Duality in information retrieval and the hypegeometric distribution (1997) 0.00 
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    Source
    Journal of documentation. 53(1997) no.5, S.499-496

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