Search (44 results, page 2 of 3)

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Abstract

Fractional frequency distributions of, for example, authors with a certain (fractional) number of papers are very irregular and, therefore, not easy to model or to explain. This article gives a first attempt to this by assuming two simple Lotka laws (with exponent 2): one for the number of authors with n papers (total count here) and one for the number of papers with n authors, n E N. Based an an earlier made convolution model of Egghe, interpreted and reworked now for discrete scores, we are able to produce theoretical fractional frequency distributions with only one parameter, which are in very close agreement with the practical ones as found in a large dataset produced earlier by Rao. The article also shows that (irregular) fractional frequency distributions are a consequence of Lotka's law, and are not examples of breakdowns of this famous historical law.

Type

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Abstract

This article studies the h-index (Hirsch index) and the g-index of authors, in case one counts authorship of the cited articles in a fractional way. There are two ways to do this: One counts the citations to these papers in a fractional way or one counts the ranks of the papers in a fractional way as credit for an author. In both cases, we define the fractional h- and g-indexes, and we present inequalities (both upper and lower bounds) between these fractional h- and g-indexes and their corresponding unweighted values (also involving, of course, the coauthorship distribution). Wherever applicable, examples and counterexamples are provided. In a concrete example (the publication citation list of the present author), we make explicit calculations of these fractional h- and g-indexes and show that they are not very different from the unweighted ones.

Type

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Abstract

Lotka's law was formulated to describe the number of authors with a certain number of publications. Empirical results (Morris & Goldstein, 2007) indicate that Lotka's law is also valid if one counts the number of publications of coauthor pairs. This article gives a simple model proving this to be true, with the same Lotka exponent, if the number of coauthored papers is proportional to the number of papers of the individual coauthors. Under the assumption that this number of coauthored papers is more than proportional to the number of papers of the individual authors (to be explained in the article), we can prove that the size-frequency function of coauthor pairs is Lotkaian with an exponent that is higher than that of the Lotka function of individual authors, a fact that is confirmed in experimental results.

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Abstract

The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based on the different possible values of the division of the L1-norm and the L2-norm of a vector. These different values yield a sheaf of increasingly straight lines which together form a cloud of points, being the investigated relation. The theoretical results are tested against the author co-citation relations among 24 informetricians for whom two matrices can be constructed, based on co-citations: the asymmetric occurrence matrix and the symmetric co-citation matrix. Both examples completely confirm the theoretical results. The results enable us to specify an algorithm that provides a threshold value for the cosine above which none of the corresponding Pearson correlations would be negative. Using this threshold value can be expected to optimize the visualization of the vector space.

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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.

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Abstract

Naranan's important theorem, published in Nature in 1970, states that if the number of journals grows exponentially and if the number of articles in each journal grows exponentially (at the same rate for each journal), then the system satisfies Lotka's law and a formula for the Lotka's exponent is given in function of the growth rates of the journals and the articles. This brief communication re-proves this result by showing that the system satisfies Zipf's law, which is equivalent with Lotka's law. The proof is short and algebraic and does not use infinitesimal arguments.

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Abstract

The notions aging, obsolescence, impact, growth, utilization, and their relations are studied. It is shown how to correct an observed citation distribution for growth, once the growth distribution is known. The relation of this correction procedure with the calculation of impact measures is explained. More interestingly, we have shown how the influence of growth on aging can be studied over a complete period as a whole. Here, the difference between the so-called average and global aging distributions is the main factor. Our main result is that growth can influence aging but that it does not cause aging. A short overview of some classical articles on this topic is given. Results of these earlier works are placed in the framework set up in this article

Type

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Abstract

In this article concentration (i.e., inequality) aspects of the functions of Zipf and of Lotka are studied. Since both functions are power laws (i.e., they are mathematically the same) it suffices to develop one concentration theory for power laws and apply it twice for the different interpretations of the laws of Zipf and Lotka. After a brief repetition of the functional relationships between Zipf's law and Lotka's law, we prove that Price's law of concentration is equivalent with Zipf's law. A major part of this article is devoted to the development of continuous concentration theory, based an Lorenz curves. The Lorenz curve for power functions is calculated and, based an this, some important concentration measures such as the ones of Gini, Theil, and the variation coefficient. Using Lorenz curves, it is shown that the concentration of a power law increases with its exponent and this result is interpreted in terms of the functions of Zipf and Lotka.

Type

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Abstract

Contrary to what one might expect, Nobel laureates and Fields medalists have a rather large fraction (10% or more) of uncited publications. This is the case for (in total) 75 examined researchers from the fields of mathematics (Fields medalists), physics, chemistry, and physiology or medicine (Nobel laureates). We study several indicators for these researchers, including the h-index, total number of publications, average number of citations per publication, the number (and fraction) of uncited publications, and their interrelations. The most remarkable result is a positive correlation between the h-index and the number of uncited articles. We also present a Lotkaian model, which partially explains the empirically found regularities.

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Abstract

In a previous work (Egghe, 2011), the first author showed that Benford's law (describing the logarithmic distribution of the numbers 1, 2, ... , 9 as first digits of data in decimal form) is related to the classical law of Zipf with exponent 1. The work of Campanario and Coslado (2011), however, shows that Benford's law does not always fit practical data in a statistical sense. In this article, we use a generalization of Benford's law related to the general law of Zipf with exponent ? > 0. Using data from Campanario and Coslado, we apply nonlinear least squares to determine the optimal ? and show that this generalized law of Benford fits the data better than the classical law of Benford.

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Abstract

This article studies the probabilities of the occurence of multi-word (m-word) phrases (m=2,3,...) in relation to the probabilities of occurence of the single words. It is well known that, in the latter case, the lae of Zipf is valid (i.e., a power law). We prove that in the case of m-word phrases (m>=2), this is not the case. We present 2 independent proof of this

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Footnote

Contribution to a thematic issue devoted to 'Theories of citation?'

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Abstract

In this article, for any group of authors, we define three different h-indices. First, there is the successive h-index h2 based on the ranked list of authors and their h-indices h1 as defined by Schubert (2007). Next, there is the h-index hP based on the ranked list of authors and their number of publications. Finally, there is the h-index hC based on the ranked list of authors and their number of citations. We present formulae for these three indices in Lotkaian informetrics from which it also follows that h2 < hp < hc. We give a concrete example of a group of 167 authors on the topic optical flow estimation. Besides these three h-indices, we also calculate the two-by-two Spearman rank correlation coefficient and prove that these rankings are significantly related.

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Abstract

Aims to inform researchers about metrics so that they become aware of the evaluative techniques being applied to their scientific output. Understanding these concepts will help them during their funding initiatives, and in hiring and tenure. The book not only describes what indicators do (or are designed to do, which is not always the same thing), but also gives precise mathematical formulae so that indicators can be properly understood and evaluated. Metrics have become a critical issue in science, with widespread international discussion taking place on the subject across scientific journals and organizations. As researchers should know the publication-citation context, the mathematical formulae of indicators being used by evaluating committees and their consequences, and how such indicators might be misused, this book provides an ideal tome on the topic. Provides researchers with a detailed understanding of bibliometric indicators and their applications. Empowers researchers looking to understand the indicators relevant to their work and careers. Presents an informed and rounded picture of bibliometrics, including the strengths and shortcomings of particular indicators. Supplies the mathematics behind bibliometric indicators so they can be properly understood. Written by authors with longstanding expertise who are considered global leaders in the field of bibliometrics

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Abstract

One aim of science evaluation studies is to determine quantitatively the contribution of different players (authors, departments, countries) to the whole system. This information is then used to study the evolution of the system, for instance to gauge the results of special national or international programs. Taking articles as our basic data, we want to determine the exact relative contribution of each coauthor or each country. These numbers are brought together to obtain country scores, or department scores, etc. It turns out, as we will show in this article, that different scoring methods can yield totally different rankings. Conseqeuntly, a ranking between countries, universities, research groups or authors, based on one particular accrediting methods does not contain an absolute truth about their relative importance

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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.

Type

a