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A graph theoretic approach from social network analysis allows size-dependent and size-independent bibliometric indicators to be identified from what is called the citation matrix. In an input-output sense, the number of references becomes the size-dependent measure of the input and the number of citations received by the journal from all journals in the network becomes the size-dependent measure of the output. However, in this paper, we are interested to compare two size-independent dimensionless indicators: the Pinski-Narin Influence Weight (IW) and the Ramanujacharyulu Power-Weakness Ratio (PWR). These are proxies for the quality of the journal’s performance in the network. We show that at the non-recursive level, the two indicators are identical. At this stage these are simply measures of popularity. After recursion (i.e. repeated improvement or iteration) these become network measures of prestige of the journals. PWR is computed as a ratio of terms in the weighted citations vector and weighted references vector after the power and weakness matrices are separately recursively iterated. The Pinski-Narin procedure computes a matrix of ratios first and evaluates the IW after recursive iteration of this matrix of ratios. In this sense, the two procedures differ just like the RoA (ratio of averages) and the AoR (Average of Ratios) ways of computing relative citation indicators. We illustrate the concepts using datasets from subgraphs of 10 statistical journals and 14 Chinese chemistry journals with network data collected from the Web of Science. We are also able to show the confounding effects when self-citations are taken into account.
2019 •
The Journal Impact Factor (JIF) is linearly sensitive to self-citations because each self-citation adds to the numerator, whereas the denominator is not affected. Pinski & Narin (1976) derived the Influence Weight (IW) as an alternative to Garfield's JIF. Whereas the JIF is based on raw citation counts normalized by the number of publications, IWs are based on the eigenvectors in the matrix of aggregated journal-journal citations without a reference to size: the cited and citing sides are combined by a matrix approach. IWs emerge as a vector after recursive iteration of the normalized matrix. Before recursion, IW is a (vector-based) non-network indicator of impact, but after recursion (i.e. repeated improvement by iteration), IWs can be considered a network measure of prestige among the journals in the (sub)graph as a representation of a field of science. As a consequence (not intended by Pinski & Narin in 1976), the self-citations are integrated at the field level and no longer...
1988 •
A quick and easy method is presented to estimate the random fluctuations exhibited by citation measures. Applying this method allows for instance a better view on the ranking of journals (their so called pecking order), when the ranks are based on the number of recieved citations or on the impact factor of the journal.
Information Processing & Management
Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics* 11976 •
Information Processing & Management
Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics1976 •
Scientometrics
The Gozinto theorem: Using citations to determine influences on a scientific publication1987 •
This paper gives a mathematical technique to study influences, using citations. Taking into account both the publications that have a direct influence and those that have an indirect influence, we obtain the total influence measure on a fixed paper.
2018 •
Recently, more and more countries are entering the global race for university competitiveness. On the one hand, global rankings are a convenient tool for quantitative analysis. On the other hand, their indicators are often difficult to quickly calculate and they often contradict each other. The author of this paper hoped to use widely available indicators for a quick analysis of the University’s publication strategy and opted for the normalized citation indicators available in the SciVal analytical tool, namely, Source Normalized Impact per Paper (SNIP) and Field-Weighted Citation Impact (FWCI). The author demonstrated the possibility of applying the correlation analysis to the impact indicators of a document and a journal on a sample of social and humanitarian fields at Peoples’ Friendship University of Russia (PFUR, “RUDN” in Russian). A dot diagram of university (or country) documents was used to form a two-factor matrix (SNIP and FWCI) that was further divided into four quadrants. Such an analysis illustrated the present situation in that discipline. An analysis of the RUDN university publications revealed problems and prospects in the development of social sciences and humanities. A serious problem observed was that high-quality results were often published in low-impact journals that narrowed the results’ potential audience and, accordingly, the number of citations. A particular attention was paid to the application of the results in practice.
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