The Simon-Yule approach to bibliometric modeling

Chen, Ye-Sho; Chong, P. Pete; Tong, Morgan Y.

doi:10.1016/0306-4573(94)90038-8

Cited by 24 publications

(12 citation statements)

References 8 publications

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“…As such, we are able to generate and explain many different types of the Bradford graphs which are reported in the literature as abnormal properties of Bradford's law. Note that this finding expands further the authors' recent work (Chen et al, 1994) where only one factor (Y is used to study the Bradford graphs.…”

Section: Summary and Discussionsupporting

confidence: 83%

Dynamic behavior of Bradford's law

Chen

Chong

Tong

1995

J. Am. Soc. Inf. Sci.

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This paper examines two problems associated with Bradford's law: (1) Since empirical data deviate from the law in many applications, what are the significant factors influencing the Bradford graphs? (2) What will be the evolution over time of the Bradford graphs? A computational analysis of the two problems is made based on Herbert Simon's model. Several significant findings about the dynamic behavior of Bradford's law are identified.

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Section: Summary and Discussionsupporting

confidence: 83%

Dynamic behavior of Bradford's law

Chen

Chong

Tong

1995

J. Am. Soc. Inf. Sci.

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“…The model was also used by Price (1976) to model the growth of reference networks and other bibliographic networks. The Yule model has also been repeatedly applied to modeling of author productivity (Chen, 1989, 1994). Several refinements to the Yule model have been suggested over the years to provide a better match of experimental data to the theoretical curve (Vukovic, 1998).…”

Section: Growth Model Of Teamsmentioning

confidence: 99%

Manifestation of research teams in journal literature: A growth model of papers, authors, collaboration, coauthorship, weak ties, and Lotka's law

Morris

Goldstein

2007

J. Am. Soc. Inf. Sci.

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This article introduces a team-based model of researchers in a specialty and investigates the manifestation of such teams in a specialty's literature. The proposed qualitative behavioral model, with its mathematical expression as a growth model, is significant because it simultaneously describes the two phenomena of collaboration and author productivity (Lotka's law) in a specialty. The model is nested: A team process models the creation of research teams and the success-breeds-success process of their production of articles, while at a lower level the productivity of authors within teams is also modeled as a success-breeds-success process. Interteam collaboration (weak ties) is modeled as random events. This simple growth model is shown to faithfully mimic six network metrics of bipartite article-author networks. The model is demonstrated on three example article collections from specialties that have a wide range of degree of collaboration: (a) a distance education collection with low collaboration degree, (b) a complex networks collection with typical collaboration degree, and (c) an atrial ablation collection with heavy collaboration degree. Introduction MotivationCollections of journal articles that cover a research specialty are collections of research reports that have been vetted by the review process. Such collections can be monitored by subject matter experts to assess the state of research progress in the specialty, and this information can be used to advise government policy makers, business leaders, and leaders in research institutions (Porter, Roper, Mason, Rossini, & Banks, 1991).A research specialty is a self-organized social organization that manifests itself in its journal literature through several types of entities: (a) articles, (b) journals, (c) article authors, (d) references, (e) terms, (f) reference authors, (g) reference journals, and more (Morris & Yen, 2004). Among these entities, article authors, hereafter referred to simply as "authors," represent the human creators of research. As such, their behavior and social organization drive all other aspects of the specialty. The productivity of authors, measured in the number of articles they publish, is an indicator of their standing and importance in the specialty. The social organization of researchers, and the pattern of assistance and information sharing that they provide one another, that is, their pattern of collaboration, form a complex network. Knowledge of the structure and dynamics of this collaboration network provides great insight into the processes driving research within a specialty, especially the processes driving research team formation, interteam collaboration, and growth to dominance of successful teams and successful researchers.Coauthorship of articles is an imperfect measure of the processes driving collaboration among researchers in a specialty (Melin & Persson, 1996;Subramanyam, 1983). However, there are many advantages to studying collaboration through coauthorship in collections of articles, with the chief advanta...

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“…The range reaches thereby from simple, but expressive indicators like the h-index [3] to complex models and algorithms like the Yule-process [4,5]. Especially in the context of New-or Alt-metrics [6,7,8,9] some concerns about citation based metrics have been expressed e.g.…”

Section: Introductionmentioning

confidence: 99%

The Scientific Influence Passivity Score (SIP)

Markscheffel

Schmidt

2014

Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

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Bibliometrics as the application of mathematical and statistical methods to books and other media of communication research [1] has already been established since a couple of years as a basis for funding decisions, and as an accepted instrument for evaluation of persons and institutions or recognizing trends and tendencies in science and technology. A wide range of methods and indicators are used to achieve this goal. In this paper, we present a new bibliometric indicator the SIP Score which is an adoption of the Influence Passivity Score (invented for the microblogging platform Twitter). We explain the adoption process, show the first results on a special created bibliographical dataset and discuss -index and its derivate).

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The Simon-Yule approach to bibliometric modeling

Cited by 24 publications

References 8 publications

Dynamic behavior of Bradford's law

Dynamic behavior of Bradford's law

Manifestation of research teams in journal literature: A growth model of papers, authors, collaboration, coauthorship, weak ties, and Lotka's law

The Scientific Influence Passivity Score (SIP)

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