-
Egghe, L.: ¬The amount of actions needed for shelving and reshelving (1996)
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- Abstract
- Discusses the number of actions (or time) needed to organize library shelves. Studies 2 types pf problem: organizing a library shelf out of an unordered pile of books, and putting an existing shelf of books in the rough order. Uses results from information theory as well as from rank order statistics (runs). Draws conclusions about the advised frequency with which these actions should be undertaken
-
Egghe, L.: On the law of Zipf-Mandelbrot for multi-word phrases (1999)
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- Source
- Journal of the American Society for Information Science. 50(1999) no.3, S.233-241
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Egghe, L.; Rousseau, R.: ¬The influence of publication delays on the observed aging distribution of scientific literature (2000)
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- Journal of the American Society for Information Science. 51(2000) no.2, S.158-165
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Egghe, L.; Liang, L.; Rousseau, R.: ¬A relation between h-index and impact factor in the power-law model (2009)
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- Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2362-2365
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Egghe, L.: Dynamic h-index : the Hirsch index in function of time (2007)
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- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.3, S.452-454
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Egghe, L.; Rousseau, R.: ¬An h-index weighted by citation impact (2008)
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- Information processing and management. 44(2008) no.2, S.770-780
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Egghe, L.: On the relation between the association strength and other similarity measures (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.7, S.1502-1504
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Egghe, L.: Informetric explanation of some Leiden Ranking graphs (2014)
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- Journal of the Association for Information Science and Technology. 65(2014) no.4, S.737-741
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Egghe, L.: Type/Token-Taken informetrics (2003)
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- Abstract
- Type/Token-Taken informetrics is a new part of informetrics that studies the use of items rather than the items itself. Here, items are the objects that are produced by the sources (e.g., journals producing articles, authors producing papers, etc.). In linguistics a source is also called a type (e.g., a word), and an item a token (e.g., the use of words in texts). In informetrics, types that occur often, for example, in a database will also be requested often, for example, in information retrieval. The relative use of these occurrences will be higher than their relative occurrences itself; hence, the name Type/ Token-Taken informetrics. This article studies the frequency distribution of Type/Token-Taken informetrics, starting from the one of Type/Token informetrics (i.e., source-item relationships). We are also studying the average number my* of item uses in Type/Token-Taken informetrics and compare this with the classical average number my in Type/Token informetrics. We show that my* >= my always, and that my* is an increasing function of my. A method is presented to actually calculate my* from my, and a given a, which is the exponent in Lotka's frequency distribution of Type/Token informetrics. We leave open the problem of developing non-Lotkaian Type/TokenTaken informetrics.
- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.7, S.603-610
-
Egghe, L.: ¬The power of power laws and an interpretation of Lotkaian informetric systems as self-similar fractals (2005)
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- Abstract
- Power laws as defined in 1926 by A. Lotka are increasing in importance because they have been found valid in varied social networks including the Internet. In this article some unique properties of power laws are proven. They are shown to characterize functions with the scalefree property (also called seif-similarity property) as weIl as functions with the product property. Power laws have other desirable properties that are not shared by exponential laws, as we indicate in this paper. Specifically, Naranan (1970) proves the validity of Lotka's law based on the exponential growth of articles in journals and of the number of journals. His argument is reproduced here and a discrete-time argument is also given, yielding the same law as that of Lotka. This argument makes it possible to interpret the information production process as a seif-similar fractal and show the relation between Lotka's exponent and the (seif-similar) fractal dimension of the system. Lotkaian informetric systems are seif-similar fractals, a fact revealed by Mandelbrot (1977) in relation to nature, but is also true for random texts, which exemplify a very special type of informetric system.
- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.7, S.669-675
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Egghe, L.: Untangling Herdan's law and Heaps' law : mathematical and informetric arguments (2007)
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- Abstract
- Herdan's law in linguistics and Heaps' law in information retrieval are different formulations of the same phenomenon. Stated briefly and in linguistic terms they state that vocabularies' sizes are concave increasing power laws of texts' sizes. This study investigates these laws from a purely mathematical and informetric point of view. A general informetric argument shows that the problem of proving these laws is, in fact, ill-posed. Using the more general terminology of sources and items, the author shows by presenting exact formulas from Lotkaian informetrics that the total number T of sources is not only a function of the total number A of items, but is also a function of several parameters (e.g., the parameters occurring in Lotka's law). Consequently, it is shown that a fixed T(or A) value can lead to different possible A (respectively, T) values. Limiting the T(A)-variability to increasing samples (e.g., in a text as done in linguistics) the author then shows, in a purely mathematical way, that for large sample sizes T~ A**phi, where phi is a constant, phi < 1 but close to 1, hence roughly, Heaps' or Herdan's law can be proved without using any linguistic or informetric argument. The author also shows that for smaller samples, a is not a constant but essentially decreases as confirmed by practical examples. Finally, an exact informetric argument on random sampling in the items shows that, in most cases, T= T(A) is a concavely increasing function, in accordance with practical examples.
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.5, S.702-709
-
Egghe, L.: Sampling and concentration values of incomplete bibliographies (2002)
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- Journal of the American Society for Information Science and technology. 53(2002) no.4, S.271-281
-
Egghe, L.; Rousseau, R.; Rousseau, S.: TOP-curves (2007)
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- Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.777-785
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Egghe, L.; Liang, L.; Rousseau, R.: Fundamental properties of rhythm sequences (2008)
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- Source
- Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1469-1478
-
Egghe, L.: ¬A rationale for the Hirsch-index rank-order distribution and a comparison with the impact factor rank-order distribution (2009)
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- Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2142-2144
-
Egghe, L.: ¬A new short proof of Naranan's theorem, explaining Lotka's law and Zipf's law (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2581-2583
-
Egghe, L.; Rousseau, R.: ¬The Hirsch index of a shifted Lotka function and its relation with the impact factor (2012)
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- Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.1048-1053
-
Egghe, L.: Remarks on the paper by A. De Visscher, "what does the g-index really measure?" (2012)
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- Journal of the American Society for Information Science and Technology. 63(2012) no.10, S.2118-2121
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Egghe, L.: Theory of the topical coverage of multiple databases (2013)
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- Journal of the American Society for Information Science and Technology. 64(2013) no.1, S.126-131
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Egghe, L.; Rousseau, R.: Aging, obsolescence, impact, growth, and utilization : definitions and relations (2000)
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- Journal of the American Society for Information Science. 51(2000) no.11, S.1004-1017
