Ronald Rousseau

Social network analysis: a powerful strategy, also for the information sciences

Social network analysis (SNA) is not a formal theory in sociology but rather a strategy for investigating social structures. As it is an idea that can be applied in many fields, we study, in particular, its influence in the information sciences. Information scientists study publication, citation and co-citation networks, collaboration structures and other forms of social interaction networks. Moreover, the Internet represents a social network of an unprecedented scale. In all these studies social network analysis can successfully be applied. SNA is further related to recent theories concerning the free market economy, geography and transport networks. The growth of SNA is documented and a co-author network of SNA is drawn. Centrality measures of the SNA network are calculated.

Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient

Author cocitation analysis (ACA), a special type of cocitation analysis, was introduced by White and Griffith in 1981. This technique is used to analyze the intellectual structure of a given scientific field. In 1990, McCain published a technical overview that has been largely adopted as a standard. Here, McCain notes that Pearsons correlation coefficient (Pearsons r) is often used as a similarity measure in ACA and presents some advantages of its use. The present article criticizes the use of Pearsons r in ACA and sets forth two natural requirements that a similarity measure applied in ACA should satisfy. It is shown that Pearsons r does not satisfy these requirements. Real and hypothetical data are used in order to obtain counterexamples to both requirements. It is concluded that Pearsons r is probably not an optimal choice of a similarity measure in ACA. Still, further empirical research is needed to show if, and in that case to what extent, the use of similarity measures in ACA that fulfill these requirements would lead to objectively better results In full-scale studies. Further, problems related to incomplete cocitation matrices are discussed.

An informetric model for the Hirsch-index

SummaryThe h-index (or Hirsch-index) was defined by Hirsch in 2005 as the number h such that, for a general group of papers, h papers received at least h citations while the other papers received no more than h citations. This definition is extended here to the general framework of Information Production Processes (IPPs), using a source-item terminology. It is further shown that in each practical situation an IPP always has a unique h-index. In Lotkaian systems h = T1/a, where T is the total number of sources and α is the Lotka exponent. The relation between h and the total number of items is highlighted.

Similarity measures in scientometric research: the Jaccard index versus Salton's cosine formula

Abstract It is shown that in most practical cases Saltons cosine formula yields a numerical value that is twice Jaccards index.

Methods for accrediting publications to authors or countries: consequences for evaluation studies

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 then 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. In addition to this, a relative increase according to one method can go hand in hand with a relative decrease according to another counting method. Indeed, we present examples in which country (or author) c has a smaller relative score in the total counting system than in the fractional counting one, yet this smaller score has a higher importance than the larger one (fractional counting). Similar anomalies were constructed for total versus proportional counts and for total versus straight counts. Consequently, a ranking between countries, universities, research groups or authors, based on one particular accrediting method does not contain an absolute truth about their relative importance. Different counting methods should be used and compared. Differences are illustrated with a real-life example. Finally, it is shown that some of these anomalies can be avoided by using geometric instead of arithmetic averages.

The scientific wealth of European nations: Taking effectiveness into account

In this study we continue the application of Data Envelopment Analysis (DEA) to assess the efficiency and effectiveness of the R&D effort of European countries. We use GDP, active population and R&D expenditure as inputs, and publications and patents as outputs. Being effective means that, in order to obtain a maximum efficiency score countries are forced to perform on every output goal. A discussion of each countrys performance and a comparison with MaysScience results concludes our analysis.

Data envelopment analysis as a tool for constructing scientometric indicators

It is shown that Data Envelopment Analysis (DEA) ca be used to construct relative scientific and technological indicators. The method is explained and illustrated using countries as objects of study; GDP, active population and R&D expenditure as inputs, and publications and patents as outputs. Using these parameters the efficiency of countries is assessed.

Co-citation, bibliographic coupling and a characterization of lattice citation networks

In this article we study directed, acyclic graphs. We introduce the head and tail order relations and study some of their properties. Recalling the notions of generalized bibliographic coupling and generalized co-citation, and introducing a new property, called the l - property, we come to a characterization of lattices. As document citation networks are concrete realizations of directed acyclic graphs all our results are directly applicable to citation analysis.

Scientific collaboration in China as reflected in co-authorship

SummaryA chronically weak area in research papers, reports, and reviews is the complete identification of background documents that formed the building blocks for these papers. A method for systematically determining these seminal references is presented. Citation-Assisted Background (CAB) is based on the assumption that seminal documents tend to be highly cited. CAB is being applied presently to three applications studies, and the results so far are much superior to those used by the first author for background development in any other study. An example of the application of CAB to the field of Nonlinear Dynamics is outlined. While CAB is a highly systematic approach for identifying seminal references, it is not a substitute for the judgement of the researchers, and serves as a supplement.

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.