Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis

Scientometrics - This study explores weighted author co-citation analysis (ACA) through a comparison of results from four weighted citation counting methods. The data set used comprises full-text...     

Intellectual structure of stem cell research: a comprehensive author co-citation analysis of a highly collaborative and multidisciplinary field

This study is an attempt to approach the intellectual structure of the stem cell research field 2004–2009 through a comprehensive author co-citation analysis (ACA), and to contribute to a better understanding of a field that has been brought to the forefront of research, therapy and political and public debates, which, hopefully, will in turn better inform research and policy. Based on a nearly complete and clean dataset of stem cell literature compiled from PubMed and Scopus, and using automatic author disambiguation to further improve results, we perform an exclusive all-author ACA of the 200 top-ranked researchers of the field by fractional citation count. We find that, despite the theoretically highly interdisciplinary nature of the field, stem cell research has been dominated by a few central medical research areas—cancer and regenerative medicine of the brain, the blood, the skin, and the heart—and a core of cell biologists trying to understand the nature and the molecular biology of stem cells along with biotechnology researchers investigating the practical identification, isolation, creation, and culturing of stem cells. It is also remarkably self-contained, drawing only on a few related areas of cell biology. This study also serves as a baseline against which the effectiveness of a range of author-based bibliometric methods and indicators can be tested, especially when based on less comprehensive datasets using less optimal analysis methods.     

Using the appearance of citations in full text on author co-citation analysis

As a frequently used method of depicting scientific intellectual structures, author co-citation analysis (ACA) has been applied to many domains. However, only count-based information is involved as the input of ACA, which is not sufficiently informative for knowledge representations. This article catches several metadata in full text of citing papers but not aims at content-level information, which increases the amount of information input to ACA without increasing computational complexity a lot. We propose a new method by involving information including the number of mentioned times in a citing paper and the number of context words in a citing sentence. We combine these pieces of information into the traditional ACA and compare the results between ACA and the proposed approach by using factor analysis, network analysis, and MDS-measurement. The result of our empirical study indicates that compared with the traditional ACA, the proposed method shows a better clustering performance in visualizations and reveals more details in displaying intellectual structures.     

Author co-citation analysis of semiconductor literature

The purpose of this study is to map semiconductor literature by author co-citation analysis in order to highlight major subject specializations in semiconductors and identify authors and their relationships within these specialties and within the field. Forty-six of the most productive authors were included in the sample list. Author samples were gathered from the INSPEC database from 1978 to 1997. The relatively low author co-citation frequencies indicate that there is a low connection among authors who publish in semiconductor journals and big differences among authors' research areas. Six sets of authors with co-citation greater than 100 times are M. Cardona and G. Lucovsky; T. Ito and K. Kobayashi; M. Cardona and G. Abstreiter; A. Y. Cho and H. Morkoc; C. R. Abernathy and W. S. Hobson; H. Morkoc and I. Akasaki. The Pearson correlation coefficient of author co-citation varies widely, i.e., from -0.17 to 0.92. This shows that some authors with high positive correlations are related in certain ways and co-cited, while other authors with high negative correlations may be rarely or never related and co-cited. Cluster analysis and multi-dimensional scaling are employed to create two-dimensional maps of author relationships in the cross-citation networks. It is found that the authors fall fairly clearly into three clusters. The first cluster covers authors in physics and its applications. The authors in the second group are experts in electrical and electronic engineering. The third group includes specialists in materials science. Because of its interdisciplinary nature and diverse subjects, semiconductor literature lacks a strong group of core authors. The field consists of several specialties around a weak center. This revised version was published online in June 2006 with corrections to the Cover Date.     

A co-citation analysis of representative authors in philosophy: Examining the relationship between epistemologists and philosophers of science

The relation between philosophy of science and epistemology is studied using the author co-citation technique. Co-citation links among 62 authors – a representative list of various styles and approaches to rationality – were established using the Arts and Humanities Citation Index. Multidimensional scaling results in a two-dimensional map of authors, where the axes represent the subject (philosophy of science to epistemology) and the method (qualitative to quantitative), respectively. The authors on the map can be clustered into more or less coherent groups at different levels of resolution.     

Using author co-citation analysis to examine the intellectual structure of e-learning: A MIS perspective

The increased use of e-learning techniques as an accepted form of teaching has resulted in a growing volume of academic research dedicated to their assessment. Despite the importance of the technique, there is little comprehensive knowledge on e-learning, especially in non-educational fields. Author co-citation analysis (ACA) is an analytical method for identifying the intellectual structure of specific knowledge domains through the relationship between two similar authors. ACA has been applied to many fields, such as information retrieval, knowledge management, and strategic management; however, it has not yet been used to analyze e-learning development. This study examines the intellectual structure of e-learning from the perspective of management information systems (MIS). By applying the ACA method, we analyze and categorize international and Taiwanese research topics into clusters. Our results show that Taiwanese authors put more effort into practical studies of business training, while international authors focus on a users’ psychological reaction to learning context. Altogether, our research provides a clear intellectual analysis of e-learning practices from 1996 to 2009, enabling us to thoroughly study and understand the influence of these techniques on modern education.     

Evaluations of context-based co-citation searching

Since machine-readable documents have become widespread, some recent studies have proposed retrieval methods using a combination of citation linkage and its context. In the case of co-citation linkage, there have been attempts to discern ‘strong’ co-citations from ‘weak’ ones by examining the positions of citations in a document. However, this promising concept has not yet been sufficiently evaluated, and it remains unclear whether search performance is significantly improved. Therefore, this paper explores the effects of using co-citation context more deeply and more widely by comparing the search performance of six retrieval methods, which differ as to whether co-citation context and normalization using cited frequency are used. For empirically evaluating the effects, a special test collection was created from CiteSeer Metadata, and the search performances of the six retrieval methods were compared by two IR metrics (AP and nDCG). The main conclusions of this paper are: (1) co-citation context has a positive effect on co-citation searching; (2) the normalization technique using cited frequency is useful for context-based co-citation searching; (3) approaches of using co-citation context tend to affect the characteristics of search performance.     

Journal co-citation analysis of semiconductor literature

The purpose of this study is to map semiconductor literature using journal co-citation analysis. The journal sample was gathered from the INSPEC database from 1978 to 1997. In the co-citation analysis, the data compiled were counts of the number of times two journal titles were jointly cited in later publications. It is assumed that the more two journals are cited together, the closer the relationship between them. The journal set used was the 30 most productive journals in the field of semiconductors. Counts of co-citations to the set of semiconductor journals were retrieved from SciSearch database, accessed through Dialog. Cluster analysis and multi-dimensional scaling were employed to create two-dimensional maps of journal relationships in the cross-citation networks. The following results were obtained through this co-citation study: The 30 journals fall fairly clearly into three clusters. The major cluster of journals, containing 17 titles, is in the subject of physics. The second cluster, consisting of 9 journals, includes journals primarily on material science. The remaining cluster represents research areas in the discipline of electrical and electronic engineering. All co-cited journals share similar co-citation profiles, reflected in high positive Pearson correlation. Two hundred and ninety-six pairs (68%) correlate at greater than 0.70. This shows that there is strong relationship between semiconductor journals. Five individual journals in five paired sets with co-citation frequency over 100,000 times include Physical Review B, Condensed Matter; Physical Review Letters; Applied Physics Letters; Journal of Applied Physics; and Solid State Communications. This revised version was published online in August 2006 with corrections to the Cover Date.     

An author co-citation analysis of information science in China with Chinese Google Scholar search engine, 2004–2006

Author co-citation analysis (ACA) is an important method for discovering the intellectual structure of a given scientific field. Since traditional ACA was confined to ISI Web of Knowledge (WoK), the co-citation counts of pairs of authors mainly depended on the data indexed in WoK. Fortunately, Google Scholar has integrated different academic databases from different publishers, providing an opportunity of conducting ACA in wider a range. In this paper, we conduct ACA of information science in China with the Chinese Google Scholar. Firstly, a brief introduction of Chinese Google Scholar is made, including retrieval principles and data formats. Secondly, the methods used in our paper are given. Thirdly, 31 most important authors of information science in China are selected as research objects. In the part of empirical study, factor analysis is used to find the main research directions of information science in China. Pajek, a powerful tool in social network analysis, is employed to visualize the author co-citation matrix as well. Finally, the resemblances and the differences between China and other countries in information science are pointed out.     

The proximity of co-citation

Traditional co-citation analysis has not taken the proximity of co-cited references into account. As long as two references are cited by the same article, they are retreated equally regardless the distance between where citations appear in the article. Little is known about what additional insights into citation and co-citation behaviours one might gain from studying distributions of co-citation in terms of such proximity. How are citations distributed in an article? What insights does the proximity of co-citation provide? In this article, the proximity of a pair of co-cited reference is defined as the nearest instance of the co-citation relation in text. We investigate the proximity of co-citation in full text of scientific publications at four levels, namely, the sentence level, the paragraph level, the section level, and the article level. We conducted four studies of co-citation patterns in the full text of articles published in 22 open access journals from BioMed Central. First, we compared the distributions of co-citation instances at four proximity levels in journal articles to the traditional article-level co-citation counts. Second, we studied the distributions of co-citations of various proximities across organizational sections in articles. Third, the distribution of co-citation proximity in different co-citation frequency groups is investigated. Fourth, we identified the occurrences of co-citations at different proximity levels with reference to the corresponding traditional co-citation network. The results show that (1) the majority of co-citations are loosely coupled at the article level, (2) a higher proportion of sentence-level co-citations is found in high co-citation frequencies than in low co-citation frequencies, (3) tightly coupled sentence-level co-citations not only preserve the essential structure of the corresponding traditional co-citation network but also form a much smaller subset of the entire co-citation instances typically considered by traditional co-citation analysis. Implications for improving our understanding of underlying factors concerning co-citations and developing more efficient co-citation analysis methods are discussed.     

A journal co-citation analysis of library and information science in China

This paper aims to reveal the relationship and structure of library and information science (LIS) journals in China. 24 core LIS journals in China are selected and the relevant data of journal co-citation are retrieved from Chinese Journal Full-Text Database constructed by China National Knowledge Infrastructure during the period of 1999–2009. By calculating mean co-citation frequencies and correlation coefficients, we find that there is a strong relationship among LIS journals in China. Utilizing the methods of cluster analysis, multidimensional scaling analysis and factor analysis, we analyze the data of journal co-citation. LIS journals in China are divided into four clusters. The relatedness among journals is shown manifestly through their locations in the two-dimensional map. A three-factor solution is obtained with the factor loading of each journal. Finally, we interpret and discuss the results to get some conclusions and also expect to describe the network characters of journal co-citation in future research.     

Applying author co-citation analysis to user interaction analysis: a case study on instant messaging groups

Author co-citation analysis (ACA) was an important method for discovering the intellectual structure of a given scientific field. There was sufficient experience that ACA would work with almost any user data that lent itself to co-occurrence. While most of the current researches still relied on the data of scientific literatures. In this study, in order to provide useful information for better enterprise management, the idea and method of ACA was applied to analyze the information interaction intensity and contents of enterprise web users. Firstly, the development of ACA was briefly introduced. Then the sample data and method used in this study were given. Three QQ groups’ instant messages of a Chinese company were selected as the raw data and the concepts and model of user interaction intensity (UII) were proposed by referring the ACA theory. Social network analysis method, combined with in-deep interview method were used to analyze the information interaction intensity and contents of enterprise users. Operatively, Excel, Ucinet, Pajek, Netdraw and VOSviewer software were combined to analyze them quantitatively and visually. Finally, it concluded that UII model was relatively reasonable and it could nicely measure the information interaction intensity and contents of enterprise web users.     

Individual co-citation clusters as nuclei of complete and dynamic informetric models of scientific and technological areas

This paper describes the construction of improved informetric models of individual scientific and technological areas on the basis of individual co-citation clusters. The developed methodology of replenishment of research front with accidentally absent papers describes more completely the model. The simple method of cluster dynamisation is proposed for the study of evolution of a research area. The transition under consideration from co-citation clusters to lexical maps of papers and patents allows to monitor the relationship between R and D in a given technological area. Pressure-swing adsorption (PSA) as modern chemical engineering, is given as an example.     

Understanding rapid theoretical change in particle physics: A month-by-month co-citation analysis

While co-citation analysis nas proved a powerful tool in the study of changes in intellectual foci in science, the technique has never been used to study very rapid changes in the theoretical structure of a scientific field. In this paper we present month-by-month co-citation analyses of key phases in the weak-electromagnetic unification research program within particle physics and show that these analyses capture and illuminate very rapid intellectual changes. These data provide yet another illustration of the utility of co-citation analysis for understanding the history of science.An earlier version of this paper was published inInformation Choices and Policies (Knowledge Industries Publications, Inc., White Plains, N. Y.: 1979), Vol. 16, Proceedings of the 42nd Annual Meeting of the American Society for Information Science. Knowledge Industries and the ASIS have kindly given permission to us to seek a wider audience. This work was made possible by NSF Grant GS-41697 to the Research Program on Social Analyses of Science Systems (SASS), Cornell University, Ithaca, New York and NSF Grant SOC76-84482 to Carleton College, Northfield, Minnesota.     

Mapping the dynamics of research networks in ecology and evolution using co-citation analysis (1975–2014)

Scientometrics - In this paper we used a co-citation network analysis to quantify and illustrate the dynamic patterns of research in ecology and evolution over 40 years (1975–2014)....     

Analysis of citation and co-citation in chemical engineering

This paper presents the results of the citation study in 24 leading journals on chemical engineering for 1987. The selective methodology or the analysis of co-citation limited only to this discipline is based on relatively low thresholds of citation and co-citation. The established research fronts refer mostly to basic research. The flow of information and knowledge to chemical engineering is determined to the extent of 70–90% by the works in this very field, as is indicated by the analysis of citations. The geography of research fronts was determined. The USSR has a very low fraction of frontal papers. This can be explained by the publication of papers in Russian and by a large number of secondary and applied research. This type of research is revealed by frequent citation of books and a small fraction of highly cited papers.     

The interdisciplinary structure of research on intercultural relations: a co-citation network analysis study

This study aims to map the content and structure of the knowledge base of research on intercultural relations as revealed in co-citation networks of 30 years of scholarly publications. Source records for extracting co-citation information are retrieved from Web of Science (1980–2010) through comprehensive keyword search and filtered by manual semantic coding. Exploratory network and content analysis is conducted (1) to discover the development of major research themes and the relations between them over time; (2) to locate representative core publications (the stars) that are highly co-cited with others and those (the bridges) connecting more between rather than within subfields or disciplines. Structural analysis of the co-citation networks identifies a core cluster that contains foundational knowledge of this domain. It is well connected to almost all the other clusters and covers a wide range of subject categories. The evolutionary path of research themes shows trends moving towards (e.g. psychology and business and economics) and away from (e.g. language education and communication) the core cluster over time. Based on the results, a structural framework of the knowledge domain of intercultural relations research is proposed to represent thematic relatedness between topical groups and their relations.     

A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis

Summary Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of “invisible colleges.”     

Bridging citation and reference distributions: Part I - The referencing-structure function and its application to co-citation and co-item studies

Citations networks are a core topic of informetrics and science studies. This article proposes to bridge the cited and citing side of citation transactions by using a disaggregated form, the “referencing-structure” function (RSF). The RSF may be also seen as the “retrieval-structure” which, in a stylized co-citation or co-word model, gives the maximum retrieval that can be expected from the bibliometric characteristics of the field (retrieval and recall features are key issues in co-citation studies). The usual citation and reference distributions may be derived from aggregates or cuts respectively, of the RSF. The RSF representation also generates new points of views on the citing-cited distributions, such as the "iso-retrieval function". A rank version of RSF is also introduced. Part I is devoted to the definition and construction of the RSF, and to the general interpretation of its various aspects in the context of co-citation studies. Generalization to other co-item (co-word, hyperlinks “co-sitations”) studies is discussed briefly. We also introduce a general form a kindred to the Weibull distribution that can be used to fit cuts of the function. The forthcoming Part II will detail empirical fits, using a few experimental files. This revised version was published online in August 2006 with corrections to the Cover Date.     

The use of bibliometric and knowledge elicitation techniques to map a knowledge domain: Software Engineering in the 1990s

Summary Parallel mappings of the intellectual and cognitive structure of Software Engineering (SE) were conducted using Author Cocitation Analysis (ACA), PFNet Analysis, and card sorting, a Knowledge Elicitation (KE) method. Cocitation counts for 60 prominent SE authors over the period 1990 - 1997 were gathered from SCISEARCH. Forty-six software engineers provided similar data by sorting authors’ names into labeled piles. At the 8 cluster level, ACA and KE identified similar author clusters representing key areas of SE research and application, though the KE labels suggested some differences between the way that the authors’ works were used and how they were perceived by respondents. In both maps, the clusters were arranged along a horizontal axis moving from “micro” to “macro” level R&D activities (correlation of X axis coordinates = 0.73). The vertical axis of the two maps differed (correlation of Y axis coordinates = -0.08). The Y axis of the ACA map pointed to a continuum of high to low formal content in published work, whereas the Y axis of the KE map was anchored at the bottom by “generalist” authors and at the top by authors identified with a single, highly specific and consistent specialty. The PFNet of the raw ACA counts identified Boehm, Basili, and Booch as central figures in subregions of the network with Boehm being connected directly or through a single intervening author with just over 50% of the author set. The ACA and KE combination provides a richer picture of the knowledge domain and provide useful cross-validation.