Raf Guns

Real and rational variants of the h-index and the g-index

The definitions of the rational and real-valued variants of the h-index and g-index are reviewed. It is shown how they can be obtained both graphically and by calculation. Formulae are derived expressing the exact relations between the h-variants and between the g-variants. Subsequently these relations are examined. In a citation context the real h-index is often, but not always, smaller than the rational h-index. It is also shown that the relation between the real and the rational g-index depends on the number of citations of the article ranked g+1. Maximum differences between h, hr and hrat on the one hand and between g, gr and grat on the other are determined.

Thoughts on uncitedness: Nobel laureates and Fields medalists as case studies

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.

Can we rank scholarly book publishers? A bibliometric experiment with the field of history

This is a publisher ranking study based on a citation data grant from Elsevier, specifically, book titles cited in Scopus history journals (2007–2011) and matching metadata from WorldCat® (i.e., OCLC numbers, ISBN codes, publisher records, and library holding counts). Using both resources, we have created a unique relational database designed to compare citation counts to books with international library holdings or libcitations for scholarly book publishers. First, we construct a ranking of the top 500 publishers and explore descriptive statistics at the level of publisher type (university, commercial, other) and country of origin. We then identify the top 50 university presses and commercial houses based on total citations and mean citations per book (CPB). In a third analysis, we present a map of directed citation links between journals and book publishers. American and British presses/publishing houses tend to dominate the work of library collection managers and citing scholars; however, a number of specialist publishers from Europe are included. Distinct clusters from the directed citation map indicate a certain degree of regionalism and subject specialization, where some journals produced in languages other than English tend to cite books published by the same parent press. Bibliometric rankings convey only a small part of how the actual structure of the publishing field has evolved; hence, challenges lie ahead for developers of new citation indices for books and bibliometricians interested in measuring book and publisher impacts.

Q-measures and betweenness centrality in a collaboration network: a case study of the field of informetrics

We study global and local Q-measures, as well as betweenness centrality, as indicators of international collaboration in research. After a brief review of their definitions, we introduce the concepts of external and internal inter-group geodesics. These concepts are applied to a collaboration network of 1129 researchers from different countries, which is based on publications in bibliometrics, informetrics, webometrics, and scientometrics (BIWS in short) from the period 1990–2009. It is thus illustrated how international collaboration (among authors from different countries) in BIWS is carried out. Our results suggest that average scores for local Q-measures are typically higher, indicating a relatively low degree of international collaboration in BIWS. The dominating form of international collaboration is bilateral, whereas multilateral collaboration is relatively rare in the field of BIWS. We also identify and visualize the most important global and local actors. Dividing the entire period in four 5-year periods, it is found that most international collaboration in the field has happened in the last time slice (2005–2009). A comparison of the different time slices reveals the non-linear growth of the indicators studied and the international expansion of the field.

Predicting and recommending collaborations: An author-, institution-, and country-level analysis

This study examines collaboration dynamics with the goal to predict and recommend collaborations starting from the current topology. Author-, institution-, and country-level collaboration networks are constructed using a ten-year data set on library and information science publications. Different statistical approaches are applied to these collaboration networks. The study shows that, for the employed data set in particular, higher-level collaboration networks (i.e., country-level collaboration networks) tend to yield more accurate prediction outcomes than lower-level ones (i.e., institution- and author-level collaboration networks). Based on the recommended collaborations of the data set, this study finds that neighbor-information-based approaches are more clustered on a 2-D multidimensional scaling map than topology-based ones. Limitations of the applied approaches on sparse collaboration networks are also discussed.

Recommending research collaborations using link prediction and random forest classifiers

We introduce a method to predict or recommend high-potential future (i.e., not yet realized) collaborations. The proposed method is based on a combination of link prediction and machine learning techniques. First, a weighted co-authorship network is constructed. We calculate scores for each node pair according to different measures called predictors. The resulting scores can be interpreted as indicative of the likelihood of future linkage for the given node pair. To determine the relative merit of each predictor, we train a random forest classifier on older data. The same classifier can then generate predictions for newer data. The top predictions are treated as recommendations for future collaboration. We apply the technique to research collaborations between cities in Africa, the Middle East and South-Asia, focusing on the topics of malaria and tuberculosis. Results show that the method yields accurate recommendations. Moreover, the method can be used to determine the relative strengths of each predictor.

A layered framework to study collaboration as a form of knowledge sharing and diffusion

Collaboration can be described using layered systems such as the article–author–institute–country structure. These structures can be considered ‘cascades’ or ‘chains’ of bipartite networks. We introduce a framework for characterizing and studying the intensity of collaboration between entities at a given level (e.g., between institutions). Specifically, we define the notions of significant, essential and vital nodes, and significant, essential and vital sub paths to describe the spread of knowledge through collaboration in such systems. Based on these notions, we introduce relative and absolute proper essential node (PEN) centrality as indicators of a nodes importance for diffusion of knowledge through collaboration.

Measuring co-authors' contribution to an article's visibility

The visibility of an article depends to a large extent on its authors. We study the question how each co-author’s relative contribution to the visibility of the article can be determined and quantified using an indicator, referring to such an indicator as a CAV-indicator. A two-step procedure is elaborated, whereby one first chooses an indicator (e.g. total number of citations, h-index …) and subsequently one of two possible approaches. The case where the indicator is an h-type index is elaborated in a Lotkaian framework. Different examples illustrate the procedure and the choices involved in determining a CAV-indicator.

Tracing the origins of the semantic web

The Semantic Web has been criticized for not being semantic. This article examines the questions of why and how the Web of Data, expressed in the Resource Description Framework (RDF), has come to be known as the Semantic Web. Contrary to previous papers, we deliberately take a descriptive stance and do not start from preconceived ideas about the nature of semantics. Instead, we mainly base our analysis on early design documents of the (Semantic) Web. The main determining factor is shown to be link typing, coupled with the influence of online metadata. Both factors already were present in early web standards and drafts. Our findings indicate that the Semantic Web is directly linked to older artificial intelligence work, despite occasional claims to the contrary. Because of link typing, the Semantic Web can be considered an example of a semantic network. Originally network representations of the meaning of natural language utterances, semantic networks have eventually come to refer to any networks with typed (usually directed) links. We discuss possible causes for this shift and suggest that it may be due to confounding paradigmatic and syntagmatic semantic relations.

Applications of the generalized law of Benford to informetric data

In a previous work (Egghe, 2011), the first author showed that Benfords 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 Benfords law does not always fit practical data in a statistical sense. In this article, we use a generalization of Benfords law related to the general law of Zipf with exponent β > 0. Using data from Campanario and Coslado, we apply nonlinear least squares to determine the optimal β and show that this generalized law of Benford fits the data better than the classical law of Benford.