Sociology Scientific Networks
by
Elisa Bellotti
  • LAST MODIFIED: 28 November 2016
  • DOI: 10.1093/obo/9780199756384-0185

Introduction

Scientific networks represent the attempt to map the structure of science using network analysis. In scientific networks, nodes can represent various entities, like individual scientists, laboratories, academic institutions, scientific journals, published articles, and even words and topics in articles. Likewise ties can represent different forms of relations, like co-authorship, citations, co-occurrence of words, intellectual and material exchange between institutions, and the like. Depending on the network feature we can thus have collaboration networks (e.g., co-authorship, but also collaborations between institutions, laboratories, and the like), citation networks (relationships among scientific publications based on their citations), and semantic networks (analyzing the occurrence of specific words in a set of publications). The network analysis approach dates back to the study of patters of communications and citations in the search for what were initially called invisible colleges, and to the study of the complexities of overlapping co-authorships and patterns of citations in the tradition of bibliometric and scientometric studies. Within this tradition researchers have attempted to map and visualize scientific collaborations in various ways as well as clustering academic disciplines according to collaboration patters, in order to observe the macro organization of scientific disciplines and topics. Scientific networks have been studied by sociologists in the attempt to discover the social factors that play a role in the work of scientists; by historians and philosophers of sciences, who look at trends in scientific epistemologies and outcomes; by physicists, mathematicians, and computer scientists, who search for theories and techniques that can handle big data and complex mechanisms; by information scientists, for the optimization of system of classification and measurement of publications; and by economists, who are interested in scientific productivity and profitability. This makes the topic of scientific networks truly interdisciplinary, where different disciplinary contributions are integrated toward the common goal of understanding the organization of and evolution of science.

General Overviews

Few journals’ special issues and books have been published providing general overviews of the substantive and methodological areas of research in studying scientific networks. Shiffrin and Börner 2004 collects contributions that use various techniques to map knowledge domains, using the same data source. Introductive articles provide general coverage of methods, techniques, and practices in the field of scientific networks. They are followed by contributions that address methods to extract and organize information from large unstructured databases; to cluster articles, citations, or co-authors in order to identify communities of science; to track dynamic changes in the network structure and to understand the processes by which such networks evolve. Finally, various articles present methods to display and visually explore the results of large-scale database analyses. Scharnhorst, et al. 2012 collects essays that review and describe major threads in the mathematical modeling of science dynamics, covering stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. Batagelj, et al. 2014 uses citation networks, co-authorship networks, and more general data on the structure of national scientific systems as empirical examples for various techniques of visualization, pattern recognition, clustering, and simulation in network analysis.

  • Batagelj, V., P. Doreian, A. Ferligoj, and N. Kejzar, eds. 2014. Understanding large temporal networks and spatial networks: Exploration, pattern searching, visualization and network evolution. Hoboken, NJ: Wiley-Blackwell.

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    This edited collection of contributions explores social mechanisms that drive network change and introduces the reader to models that detect patterns of changing structures in large temporal networks and spatial networks, using scientific networks as empirical examples.

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    • Scharnhorst, A., K. Börner, and P. van den Besselaar, eds. 2012. Models of science dynamics. Encounters between complexity theory and information. Berlin and Heidelberg, Germany: Springer-Verlag.

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      The edited book reviews and describes major threads in the mathematical modeling of science dynamics, with the intention of reaching a unifying framework in studying the structure and evolution of science. The book is introduced by chapters that define and operationalize the terminology used in the study of science, and review the history of mathematical approaches for the modeling of science.

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      • Shiffrin, R. M., and K. Börner, eds. 2004. Mapping knowledge domains. Proceedings of the National Academy of Sciences 101. Suppl. 1.

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        A collection of essays, published in a Proceedings of the National Academy of Sciences (PNAS) supplement, presented at the Arthur M. Sackler Colloquium on Mapping Knowledge Domains exploring and applying the techniques available to map knowledge domains using an electronic compilation of the full text documents from PNAS covering 7 January 1997 to 7 September 2002 (148 issues containing some 93,000 journal pages).

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        Classic Works

        The study of scientific networks and of their influence in the evolution of knowledge domains was prompted by seminal works that overcome the idea of science as a mere accumulation of objective discoveries. Scholars in the 1960s started seeing knowledge as a cultural object produced and validated by communities of researchers: as such, scientific communities are influenced and shaped by the social, political, and institutional context in which they are embedded. Classic works of Derek de Solla Price (de Solla Price 1963; de Solla Price 1965), Eugene Garfield (Garfield 1955), and Diana Crane (Crane 1972) investigate the evolution of science from different disciplinary perspectives: Garfield 1955 applies linguistic and computational techniques to the study of scientific publications, eventually inventing the Science Citation Index and founding the disciplines of bibliometrics and scientometrics; de Solla Price 1963 and de Solla Price 1965 explore the organization of science in search of physics laws that could explain the exponential increase in scientific productivity; and Crane 1972 analyzes the patterns of communications and collaborations between scientists in order to reveal the social organization of research areas. Small 1973 introduces the measure of co-citations between scientific articles, while Beaver and Rosen’s trilogy of articles, beginning with Beaver and Rosen 1978, develop a comprehensive theory of scientific collaborations based on co-authorship analysis.

        • Beaver, D., and R. Rosen. 1978. Studies in scientific collaboration: Part I. The professional origins of scientific co-authorship. Scientometrics 1.1: 65–84.

          DOI: 10.1007/BF02016840Save Citation »Export Citation »E-mail Citation »

          Part II: “Scientific co-authorship, research productivity and visibility in the French scientific elite,” Scientometrics 1.2 (1979): 133–149; Part III: “Professionalization and natural history of modern scientific co-authorship,” Scientometrics 1.3 (1979): 231–245. Beaver and Rosen’s trilogy of articles develops a comprehensive theory of scientific collaboration based on co-authorship analysis. The authors link the emergence of co-authorship practices with the professionalization of science, and observe that collaborations mostly happen within scientific elites. They also notice that collaborations increase individual research productivity and enhance the visibility of research to the larger scientific community, and conclude that collaborations serve as a means of professional mobility.

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          • Crane, D. 1972. Invisible colleges. Chicago: Univ. Press Chicago.

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            In this book, Crane compares the network of co-authorships in the fields of rural sociology and mathematics, exploring the social organization of the invisible colleges which were already the focus of attention of de Solla Price 1963. After identifying the scholars who published in each area, she investigates their formal and informal relationships within their respective research.

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            • Garfield, E. 1955. Citation indexes for science: A new dimension in documentation through association of ideas. Science 122:108–111.

              DOI: 10.1126/science.122.3159.108Save Citation »Export Citation »E-mail Citation »

              In this work, Garfield first proposes a bibliographic system for scientific literature that became the main instrument to evaluate the impact factor of scientific works and provided the necessary data to initiate the disciplines of scientometric and bibliometric, enabling the statistical analysis of the scientific literature on a large scale.

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              • Small, H. 1973. Co-citation in the scientific literature: A new measure of the relationship between publications. Journal of the American Society for Information Science 24:265–269.

                DOI: 10.1002/asi.4630240406Save Citation »Export Citation »E-mail Citation »

                Small introduces the measure of co-citations, which looks at the number of times two articles are cited together by other articles. Networks of co-cited papers are used to identify cores of scientific specialties, and the method is illustrated on an example from the literature of particle physics.

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                • de Solla Price, D. J. 1963. Little science, big science. New York: Columbia Univ. Press.

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                  Little Science, Big Science explores the exponential growth of resources dedicated to science and the transformation of the scale of scientific organizations. It details the changes in magnitude of the number of people, the volume of publications, the amount of resources, the size of scientific organizations and the scale of machineries that happened during and after the II World War.

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                  • de Solla Price, D. J. 1965. Networks of scientific papers. Science 149.3683: 510–515.

                    DOI: 10.1126/science.149.3683.510Save Citation »Export Citation »E-mail Citation »

                    This seminal work is the first quantitative study of the network of citations between scientific papers, in which de Solla Price discovers that both the in- and out-degrees of a citation network have power-law distributions, therefore opening up the route of the studies of scale-free networks.

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                    Journals

                    There are journals specifically dedicated to research on scientific networks. Scientometrics is the most specialized one, focusing on theories, methods and empirical finding of scientometric and bibliometric studies. The Journal of the Association for Information Science and Technology (previously called the Journal of the American Society for Information Science) is another important source that approaches the topic of scientific networks from the disciplinary framework of information science. Social Networks has published several studies on co-authorship, citation analysis, and scientific networks in general. Social Studies of Science has published several works on citation analysis, co-authorship, and co-word analysis, especially in the 1970s and 1980s; Research Policy publishes research that deals with innovation, technology, research and development (R&D), and science in general, including studies that adopt a network perspective. All journals are of interdisciplinary nature.

                    • Journal of the Association for Information Science and Technology. 1950–.

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                      JASIST is the official publication of the Association for Information Science and Technology (ASIS&T) and has been published monthly since 1950 (although the number of issues has increased from four to twelve along the decades). It is the leading journal for information studies, and publishes rigorous work of an empirical, experimental, ethnographic, conceptual, historical, socio-technical, policy-analytic, or critical-theoretical nature, together with exhaustive review articles in the special section of “Advances in Information Science” (AIS).

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                      • Research Policy. 1972–.

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                        Published since 1972, the journal is devoted to analyzing, understanding, and responding to the economic, policy, management, organizational, environmental, and other challenges posed by innovation, technology, R&D and science. With ten issues per year, it has a more economic approach, although its multidisciplinary aims make it the leading journal in innovation studies.

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                        • Scientometrics. 1978–.

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                          The journal was founded in 1978 by Tibor Braun, and became the main venue for scientometric and bibliometric studies. Published quarterly, it aims to study the development and mechanism of science using statistical and mathematical methods.

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                          • Social Networks.

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                            Social Networks is a quarterly journal that since 1979 has published studies on the empirical structure of social relations and networks. While its aims go beyond the substantive area of science studies, the journal has published relevant methodological advancements in the study of scientific networks.

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                            • Social Studies of Sciences. 1971–.

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                              First issued in 1971, the bimonthly journal publishes original research on science, technology, and medicine. While in the 1970s and 1980s it heavily published articles on co-authorship and citation analysis, in recent years it has broadened the theoretical and methodological perspectives in the study of science, proposing qualitative studies and research grounded in actor network theory.

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                              Substantive Topics in the Study of Scientific Networks

                              This section summarizes the substantive areas of research that have developed around the study of scientific networks, and provides indicative references for each of these areas.

                              Citation Analysis

                              Empirical research on citation analysis can be dated back to the pioneer study of Garfield and colleagues on the development of DNA discoveries (Garfield, et al. 1964). Citation analysis is mainly used to locate disciplinary and specialities’ boundaries, and to map their evolution, like in the study of Garfield, et al. 1964; Hummon and Doreian 1989; Hummon and Carley 1993; and Kajikawa, et al. 2007; in some cases scholars have attempted to cross the disciplinary or speciality boundaries, like in Hargens, et al. 1980 and White, et al. 2004. Other references focus on the comparison and evaluation of clustering methods applied to citations and co-citations, like Gmür 2003, or on the limits of quantitative methods and citation analysis in representing the organization of science (Arunachalam and Manorama 1989).

                              • Arunachalam, S., and K. Manorama. 1989. Are citation-based quantitative techniques adequate for measuring science on the periphery? Scientometrics 15.5: 393–408.

                                DOI: 10.1007/BF02017061Save Citation »Export Citation »E-mail Citation »

                                Discussion of the problems of inadequacies of citation analysis-based quantitative techniques in the context of developing countries, using India as a case study.

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                                • Garfield, E., I. H. Sher, and R. J. Torpie. 1964. The use of citation data in writing the history of science. Philadelphia: Institute for Scientific Information.

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                                  Case study of the development of the genetic code using citation analysis.

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                                  • Gmür, M. 2003. Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics 57.1: 27–57.

                                    DOI: 10.1023/A:1023619503005Save Citation »Export Citation »E-mail Citation »

                                    A summary of the state of art of co-citation analysis which presents several methods of clustering references: these methods are then trialed on organization studies literature.

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                                    • Hargens, L. L., N. C. Mullins, and P. K. Hecht. 1980. Research areas and stratification processes in science. Social Studies of Science 10:55–74.

                                      DOI: 10.1177/030631278001000103Save Citation »Export Citation »E-mail Citation »

                                      Study of two research areas in biomedical science identified and delimited by co-citation analysis.

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                                      • Hummon, N. P., and K. Carley. 1993. Social networks as normal science. Social Networks 15:71–106.

                                        DOI: 10.1016/0378-8733(93)90022-DSave Citation »Export Citation »E-mail Citation »

                                        Case study on the pattern of citations in the first twelve volumes of the journal Social Networks. Using main path analysis the authors found six main path structures of connected articles that define and describe core developments in the specialty of social network analysis.

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                                        • Hummon, N. P., and P. Doreian. 1989. Connectivity in a citation network: The development of DNA theory. Social Networks 11:39–63.

                                          DOI: 10.1016/0378-8733(89)90017-8Save Citation »Export Citation »E-mail Citation »

                                          Analysis of the citation network that links the key events and papers that lead to the discovery and modeling of DNA. The authors propose three indices that can efficiently identify the main path in the citation network.

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                                          • Kajikawa, Y., J. Ohno, Y. Takeda, K. Matsushima, and H. Komiyama. 2007. Creating an academic landscape of sustainability science: An analysis of the citation network. Sustainability Science 2.2: 221–231.

                                            DOI: 10.1007/s11625-007-0027-8Save Citation »Export Citation »E-mail Citation »

                                            Exploration of the citation network of sustainability science, using a topological clustering method.

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                                            • White, H. D., B. Wellman, and N. Nazer. 2004. Does citation reflect social structure? Longitudinal evidence from the “globenet” interdisciplinary. Journal of the American Society for Information Science and Technology 55.2: 111–126.

                                              DOI: 10.1002/asi.10369Save Citation »Export Citation »E-mail Citation »

                                              Analysis of the relation between personal relationships between scholars and how they cite each other (intercited) in journal articles, using the case study of an international group of sixteen researchers from seven disciplines that was established in 1993 to study human development.

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                                              Co-Authorship

                                              Co-authorship is a commonly used approach to measure scientific collaborations. Studies map and analyze networks in single country, for example, Chinchilla-Rodríguez, et al. 2012 in Argentina, while others attempt to map international relations that bridge across national environments, like Glänzel and Schubert 2005. Studies have looked at collaborations within a single discipline, like Cainelli, et al. 2015; or for a specific language, like Ossenblok, et al. 2014. Ozel, et al. 2014 turn the attention to the impact of gender in establishing co-authorship using Turkey as a case study. Another stream of research looks at collaborations between academia and industry, for example the study of Lundberg, et al. 2006. Finally, some works evaluate the methodological robustness of co-authorship analysis, like Lindsey 1980 and De Stefano, et al. 2013.

                                              • Cainelli, G., M. A. Maggioni, T. E. Uberti, and A. de Felice. 2015. The strength of strong ties: How co-authorship affect productivity of academic economists? Scientometrics 102.1: 673–699.

                                                DOI: 10.1007/s11192-014-1421-5Save Citation »Export Citation »E-mail Citation »

                                                Analysis of the effects of co-authorship on the scientific output of Italian economists.

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                                                • Chinchilla-Rodríguez, Z., A. Ferligoj, S. Miguel, L. Kronegger, and F. de Moya-Anegón. 2012. Blockmodeling of co-authorship networks in library and information science in Argentina: A case study. Scientometrics 93.3: 699–717.

                                                  DOI: 10.1007/s11192-012-0794-6Save Citation »Export Citation »E-mail Citation »

                                                  The paper introduces the use of blockmodeling in the study of the internal structure of Argentinian co-authorship networks over time.

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                                                  • De Stefano, D., V. Fuccella, M. P. Vitale, and S. Zaccarin. 2013. The use of different data sources in the analysis of co-authorship networks and scientific performance. Social Networks 35.3: 370–381.

                                                    DOI: 10.1016/j.socnet.2013.04.004Save Citation »Export Citation »E-mail Citation »

                                                    Comparison of three datasets and the effects of using them in the analysis of co-authorship networks among Italian statisticians.

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                                                    • Glänzel, W., and A. Schubert. 2005. Domesticity and internationality in co-authorship, references and citations. Scientometrics 65.3: 323–342.

                                                      DOI: 10.1007/s11192-005-0277-0Save Citation »Export Citation »E-mail Citation »

                                                      Analysis of the national and international character of citation behavior of thirty-six countries and comparison with international co-authorship patterns.

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                                                      • Lindsey, D. 1980. Production and citation measures in the sociology of science: The problem of multiple authorship. Social Studies of Science 10.2: 145–162.

                                                        DOI: 10.1177/030631278001000202Save Citation »Export Citation »E-mail Citation »

                                                        The study addresses the problem of multiple authorships in the measurement of publications and citations and presents a revision of current indices.

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                                                        • Lundberg, J., G. Tomson, I. Lundkvist, and J. S. Mats Brommels. 2006. Collaboration uncovered: Exploring the adequacy of measuring university-industry collaboration through co-authorship and funding. Scientometrics 69.3: 575–589.

                                                          DOI: 10.1007/s11192-006-0170-5Save Citation »Export Citation »E-mail Citation »

                                                          The study assesses how well university-industry collaborations can be identified and described using co-authorship data, comparing them with industrial funding to a medical university.

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                                                          • Ossenblok, T. L. B., F. T. Verleysen, and T. C. E. Engels. 2014. Co-authorship of journal articles and book chapters in the social sciences and humanities (2000–2010). Journal of the Association for Information Science and Technology 65.5: 882–897.

                                                            DOI: 10.1002/asi.23015Save Citation »Export Citation »E-mail Citation »

                                                            Analysis of co-authorship patterns in the social sciences and using the Flemish Academic Bibliographic Database. The study combines data on journal articles and book chapters.

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                                                            • Ozel, B., H. Kretschmer, and T. Kretschmer. 2014. Co-authorship pair distribution patterns by gender. Scientometrics 98.1: 703–723.

                                                              DOI: 10.1007/s11192-013-1145-ySave Citation »Export Citation »E-mail Citation »

                                                              Analysis of the impact of gender both on publication productivity and on patterns of scientific collaborations in social sciences in Turkey.

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                                                              Co-Word, Semantic, and Content Analysis

                                                              These areas of research focus on the content of scientific articles, measuring topics’ similarities by looking at the frequency in which topics occur and at the proximity of words in published materials. Such methods are particularly useful to delimitate disciplinary boundaries and to explore interdisciplinary research areas. Most of the references focus on a specific topic, like polymer science (Callon, et al. 1983), biomedical science (Lievrouw, et al. 1987), neural network research (Van Raan and Tijssen 1993), Condensed Matter Physics (CMP) (Bhattacharya and Basu 1998), Library and Information Science (Milojević, et al. 2011), waste recycling (WR) research (Garechana, et al. 2014). However they combine co-word analysis with various analytical techniques, like systematic content analysis (Callon, et al. 1983), block modeling (Bhattacharya and Basu 1998), or hierarchical clustering, multidimensional scaling, and trends in usage of terms (Milojević, et al. 2011). Hu, et al. 2013 combine co-word analysis, multivariate statistical analysis, and social network analysis to investigate scientific production in China, while Lievrouw and colleagues triangulate co-word analysis of grants with co-citation analysis of publications. Zhang, et al. 2013 proposes an overall framework for the analysis of citation content.

                                                              • Bhattacharya, S., and P. K. Basu. 1998. Mapping a research area at the micro level using co-word analysis. Scientometrics 43.3: 359–372.

                                                                DOI: 10.1007/BF02457404Save Citation »Export Citation »E-mail Citation »

                                                                Case study of publications in Condensed Matter Physics (CMP) in two time-periods using co-word analysis. Words’ linkages are analyzed through network analysis methods, and dynamics are investigated through structurally equivalent blocks.

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                                                                • Callon, M., J. P. Courtial, W. A. Turner, and S. Bauin. 1983. From translations to problematic networks: An introduction to co-word analysis. Social Science Information 22.2: 191–235.

                                                                  DOI: 10.1177/053901883022002003Save Citation »Export Citation »E-mail Citation »

                                                                  The study illustrates how co-word analysis techniques can be used to study interactions between academic and technological research. It uses systematic content analysis of publications in the field of polymer science to show the evolution of various subject areas in this field.

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                                                                  • Garechana, G., R. Rio-Belver, E. Cilleruelo, and J. Larruscain Sarasola. 2014. Clusterization and mapping of waste recycling science. Evolution of research from 2002 to 2012. Journal of the Association for Information Science and Technology 66.7: 1431–1446.

                                                                    DOI: 10.1002/asi.23264Save Citation »Export Citation »E-mail Citation »

                                                                    Case study of waste recycling (WR) research using authors’ keywords co-occurrence, whose similarities are analyzed with cluster analysis to reveal the main topics addressed by the scientific community.

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                                                                    • Hu, C. P., J. M. Hu, S. J. Deng, and Y. Liu. 2013. A co-word analysis of library and information science in China. Scientometrics 97.2: 369–382.

                                                                      DOI: 10.1007/s11192-013-1076-7Save Citation »Export Citation »E-mail Citation »

                                                                      Case study of the structure of scientific publications in China using co-word analysis, multivariate statistical analysis and social network analysis.

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                                                                      • Lievrouw, L. A., M. R. Everett, C. U. Lowe, and E. Nadel. 1987. Triangulation as a research strategy for identifying invisible colleges among biomedical scientists. Social Networks 9.3: 217–248.

                                                                        DOI: 10.1016/0378-8733(87)90021-9Save Citation »Export Citation »E-mail Citation »

                                                                        Triangulation study of biomedical scientists that combines co-word analysis of grants with scientists’ social networks and co-citation analysis of publications.

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                                                                        • Milojević, S., C. R. Sugimoto, E. Yan, and Y. Ding. 2011. The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology 62.10: 1933–1953.

                                                                          DOI: 10.1002/asi.21602Save Citation »Export Citation »E-mail Citation »

                                                                          Study of the cognitive structure of Library and Information Science using co-word analysis and hierarchical clustering, multidimensional scaling, and trends in usage of terms.

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                                                                          • Van Raan, F. J., and R. J. W. Tijssen. 1993. The neural net of neural network research. Scientometrics 26.1: 169–192.

                                                                            DOI: 10.1007/BF02016799Save Citation »Export Citation »E-mail Citation »

                                                                            Case study of neural network research analyzed via co-word analysis, to assess if bibliometric mapping can suggest novel links between pieces of knowledge and therefore stimulate the emergence of new ideas in specific research areas.

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                                                                            • Zhang, G., Y. Ding, and S. Milojević. 2013. Citation content analysis (CCA): A framework for syntactic and semantic analysis of citation content. Journal of the American Society for Information Science and Technology 64.7: 1490–1503.

                                                                              DOI: 10.1002/asi.22850Save Citation »Export Citation »E-mail Citation »

                                                                              The article proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content.

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                                                                              Patents

                                                                              A stream of research has developed that looks not only at the organization and academic output of research, but also at its relationship with industrial innovation and economic development. Studies investigate how collaborations to research projects impact on the dimension of patenting and technological innovations, mainly by looking at the European Framework Programme of funding. Breschi and Cusmano 2003 observes the evolution of research collaborations between universities and the private sector and the emergence of an oligarchic core of research institutions. Scherngell and Barber 2011 and Scherngell and Lata 2013 measure the impact of geographical and technological distances on the evolution of research collaboration. Maggioni, et al. 2014 analyzes how the innovative performance of a region, measured as patenting intensity, relates to its region-specific knowledge production, and to the knowledge production of neighboring regions, while Maggioni and Uberti 2007 relate the network of knowledge production in several European Union (EU) countries to geographical, financial, and sectorial distances. Protogerou, et al. 2010 tests the evolution of small worlds and scale-free networks in the structure of collaborations to EU projects over time.

                                                                              • Breschi, S., and L. Cusmano. 2003. Unveiling the texture of a European research area: Emergence of oligarchic networks under EU framework programmes. International Journal of Technology Management 27:747–772.

                                                                                DOI: 10.1504/IJTM.2004.004992Save Citation »Export Citation »E-mail Citation »

                                                                                Study of the evolution of research collaborations between universities and the private sector and the emergence of an oligarchic core of research institutions.

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                                                                                • Maggioni, M. A., M. Nosvelli, and T. E. Uberti. 2014. Does intentional mean hierarchical? Knowledge flows and innovative performance of European regions. Annals of Regional Science 53:453–485.

                                                                                  DOI: 10.1007/s00168-014-0618-0Save Citation »Export Citation »E-mail Citation »

                                                                                  Analysis of how the innovative performance of a region, measured as patenting intensity, relates to its region-specific knowledge production, and to the knowledge production of neighboring regions.

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                                                                                  • Maggioni, M. A., and T. E. Uberti. 2007. Inter-regional knowledge flows in Europe: An econometric analysis. In Applied evolutionary economics and economic geography. Edited by K. Frenken, 230–255. Cheltenham, UK: Edward Elgar.

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                                                                                    The study observes the relation between the network of knowledge production in several EU countries and their geographical, financial, and sectorial distances.

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                                                                                    • Protogerou, A., Y. Caloghirou, and E. Siokas. 2010. Policy-driven collaborative research networks in Europe. Economics of Innovation and New Technology 19:349–372.

                                                                                      DOI: 10.1080/10438590902833665Save Citation »Export Citation »E-mail Citation »

                                                                                      The article analyzes research collaborations to EU projects from 1994 to 2006, and demonstrates that the field of research structure is a scale-free network dominated by preferential attachment law.

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                                                                                      • Scherngell, T., and M. J. Barber. 2011. Distinct spatial characteristics of industrial and public research collaborations: Evidence from the fifth EU Framework Programme. Annals of Regional Science 46:247–266.

                                                                                        DOI: 10.1007/s00168-009-0334-3Save Citation »Export Citation »E-mail Citation »

                                                                                        The authors compare the research collaboration to EU projects between universities and industries.

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                                                                                        • Scherngell, T., and R. Lata. 2013. Towards an integrated European research area? Findings from Eigenvector filtered spatial interaction models using European Framework Programme data. Papers in Regional Science 92:555–577.

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                                                                                          The article measures the impact of geographical and technological distances on the evolution of research collaboration.

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                                                                                          Comparison of Representations

                                                                                          There is an interesting stream of research dedicated to compare variously constructed networks for the representation of scientific production. Braam, et al. 1999, for example, compares co-citation analysis and content analysis to test their congruence in mapping specialities of scientific research; Yan and Ding 2012 observes how variously constructed scientific networks relate to each other, and similarly Qiu, et al. 2014 measures the correlation of five types of author co-occurrence networks.

                                                                                          • Braam, R. R., H. F. Moed, and A. F. J. van Raan. 1999. Mapping of science by combined co-citation and word analysis. I. Structural aspects. Journal of the American Society for Information Science 42.4: 233–251.

                                                                                            DOI: 10.1002/(SICI)1097-4571(199105)42:4<233::AID-ASI1>3.0.CO;2-ISave Citation »Export Citation »E-mail Citation »

                                                                                            The study compares co-citation analysis and quantitative analysis of content-words in their effectiveness and congruence of mapping subject-matter specialties of scientific research.

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                                                                                            • Qiu, J. P., K. Dong, and H. Q. Yu. 2014. Comparative study on structure and correlation among author co-occurrence networks in bibliometrics. Scientometrics 101.2: 1345–1360.

                                                                                              DOI: 10.1007/s11192-014-1315-6Save Citation »Export Citation »E-mail Citation »

                                                                                              The study analyzes the potential correlation and the difference among five types of author co-occurrence networks: co-authorship; author co-citation; author bibliographic coupling; words-based author coupling; journals-based author coupling.

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                                                                                              • Yan, E., and Y. Ding. 2012. Scholarly network similarities: How bibliographic coupling networks, citation networks, co-citation networks, topical networks, co-authorship networks, and co-word networks relate to each other. Journal of the American Society for Information Science and Technology 63.7: 1313–1326.

                                                                                                DOI: 10.1002/asi.22680Save Citation »Export Citation »E-mail Citation »

                                                                                                The study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, co-citation networks, topical networks, co-authorship networks, and co-word networks.

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                                                                                                Maps and Visualizations

                                                                                                An important area of research in scientific networks is dedicated to the attempts to map and visualize them. The task is not trivial, as scientific networks are large and complex, and good visualizations are necessary for understanding the topography of science. Börner, et al. 2003 offers a useful overview of techniques available for mapping and visualizing knowledge domains, while Kretschmer, et al. 2015 describes various methods for three-dimensional visualization of co-authorship networks. Brandes and Pich 2011 propose a method to visualize shared references in bibliographic data based on a distance metric subjected to multidimensional scaling. Boyack, et al. 2005 attempt the ambitious task of visualizing the entire field of science by looking at citations patterns.

                                                                                                • Börner, K., C. Chen, and K. Boyack. 2003. Visualizing knowledge domains. In Annual review of information science & technology. Edited by B. Cronin, 37. Medford, NJ: Information Today.

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                                                                                                  The chapter provides an overview of the many techniques used in the process of mapping and visualizing knowledge domains. It summarizes the history of research on visualizing knowledge domains and reviews measures and approaches to determine bibliographic, linguistic, co-word, co-term, co-classification, content, or semantic similarities.

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                                                                                                  • Boyack, K. W., R. Klavans, and K. Börner. 2005. Mapping the backbone of science. Scientometrics 64.3: 351–374.

                                                                                                    DOI: 10.1007/s11192-005-0255-6Save Citation »Export Citation »E-mail Citation »

                                                                                                    The paper aims to construct a map representing the structure of all of science based on citations. It applies eight alternative measures of journal similarity and for each of them generates two-dimensional spatial layouts while measuring the structural accuracy for each map.

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                                                                                                    • Brandes, U., and C. Pich. 2011. Explorative visualization of citation patterns in social network research. Journal of Social Structure 12.8.

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                                                                                                      The paper proposes a visual representation of bibliographic data based on shared references. The method employs a distance metric that is derived from bibliographic coupling and then subjected to fast approximate multidimensional scaling. Its utility is demonstrated by an explorative analysis of social network publications.

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                                                                                                      • Kretschmer, H., D. Beaver, and T. Kretschmer. 2015. Three-dimensional visualization and animation of emerging patterns by the process of self-organization in collaboration networks. Scientometrics 104.1: 87–120.

                                                                                                        DOI: 10.1007/s11192-015-1579-5Save Citation »Export Citation »E-mail Citation »

                                                                                                        The paper describes several methods for three-dimensional modeling and animation and applies these methods to two co-authorship networks.

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                                                                                                        Methodological Approaches

                                                                                                        Various methods have been developed to analyze scientific networks. The following list provides some examples of the most common analytical strategies.

                                                                                                        Clustering

                                                                                                        Several clustering procedures have been applied to the study of scientific networks, to search for similarities in the profiles of authors, articles, journals, disciplines, and even countries.

                                                                                                        • Boyack, K. W., and R. Klavans. 2010. Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology 61.12: 2389–2404.

                                                                                                          DOI: 10.1002/asi.21419Save Citation »Export Citation »E-mail Citation »

                                                                                                          A comparison of the accuracies of cluster solutions of a corpus of articles from the biomedical literature using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach.

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                                                                                                          • Chen, C., T. Cribbin, R. Macredie, and S. Morar. 2002. Visualizing and tracking the growth of competing paradigms: Two case studies. Journal of the American Society for Information Science and Technology 53.8: 678–689.

                                                                                                            DOI: 10.1002/asi.10075Save Citation »Export Citation »E-mail Citation »

                                                                                                            Clustering procedures are used to track and visualize the development of scientific paradigms. The growth of a paradigm is depicted and animated through the analysis of the position and movement of the core cluster of citation within the co-citation network.

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                                                                                                            • Chen, C., F. Ibekwe-SanJuan, and J. Hou. 2010. The structure and dynamics of co-citation clusters: A multiple-perspective cocitation analysis. Journal of the American Society for Information Science and Technology 61.7: 1386–1409.

                                                                                                              DOI: 10.1002/asi.21309Save Citation »Export Citation »E-mail Citation »

                                                                                                              A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method integrates network visualization, spectral clustering, automatic cluster labeling, and text summarization.

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                                                                                                              • Kretschmer, H. 2004. Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the Web. Scientometrics 60.3: 409–420.

                                                                                                                DOI: 10.1023/B:SCIE.0000034383.86665.22Save Citation »Export Citation »E-mail Citation »

                                                                                                                Proposal of an improvement of the “Erdős Number” method that controls the distribution of highly productive authors among the clusters in the co-authorship network for the size of the clusters.

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                                                                                                                • Kronegger, L., F. Mali, A. Ferligoj, and P. Doreian. 2015. Classifying scientific disciplines in Slovenia: A study of the evolution of collaboration structures. Journal of the Association for Information Science and Technology 66.2: 321–339.

                                                                                                                  DOI: 10.1002/asi.23171Save Citation »Export Citation »E-mail Citation »

                                                                                                                  Case study of the network of co-authorship in Slovenia: disciplines are clustered according to their tendencies to co-author within the same discipline, across different disciplines, and outside Slovenia.

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                                                                                                                  • Zhang, L., F. Janssens, L. Liang, and W. Glänzel. 2010. Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research. Scientometrics 82.3: 687–706.

                                                                                                                    DOI: 10.1007/s11192-010-0180-1Save Citation »Export Citation »E-mail Citation »

                                                                                                                    Proposal of a clustering algorithm based on journal cross-citation to validate and to improve the journal-based subject classification schemes.

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                                                                                                                    Ranking

                                                                                                                    A relevant issue in scientific network is to measure the impact of publications. Probably the most widely known quality measure is the impact factor (Garfield 1955, cited under Classic Works, and see also Citation Analysis), which simply counts the yearly average number of citations of articles published in a journal. Other measures have been proposed to address the limits of the impact factor and refine ranking measurements (for a comparison of thirty-nine measures see Bollen, et al. 2009), some of which take into account the structure of the network of citations between publications. Bergstrom 2007 uses eigenvector centrality to calculate the importance of a journal based on the importance of the journals citing it. Bollen, et al. 2007 proposes a weighted version of PageRank algorithm that reflects the prestige of a journal, and that combined with its popularity produces a summative measure (Y factor) of both popularity and prestige. PageRank measures are further extended by Fiala, et al. 2008 and by Walker, et al. 2007. Yan, et al. 2011 advances the measure of prestige by proposing a P-Ranks indicator that differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Palla, et al. 2015 combines flow hierarchy, where the nodes can be layered in different levels so that the nodes that are influenced by a given node are at lower levels, with nested hierarchy, where higher level elements contain lower level elements. Ding, et al. 2009 tests the relationship between different ranking measures like PageRanks, h-index, citation ranking and centrality measures on 108 most influential authors’ co-citation network.

                                                                                                                    • Bergstrom, C. T. 2007. Eigenfactor: Measuring the value and prestige of scholarly journals. College & Research Libraries News 68.5: 314–316.

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                                                                                                                      The study proposes an iterative ranking algorithm that measure the influence of a journal based on the influence of the journals citing it.

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                                                                                                                      • Bollen, J., M. A. Rodriguez, and H. Van de Sompel. 2007. Journal status. Scientometrics 69.3: 669–687.

                                                                                                                        DOI: 10.1007/s11192-006-0176-zSave Citation »Export Citation »E-mail Citation »

                                                                                                                        The article distinguishes between journal’s popularity, as the number of citations it receives, and journal’s prestige, as the quality of the journals citing it. It then demonstrates that a weighted version of the popular PageRank algorithm can be used to obtain a metric that reflects prestige.

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                                                                                                                        • Bollen, J., H. Van de Sompel, A. Hagberg, and R. Chute. 2009. A principal component analysis of 39 scientific impact measures. PLoS ONE 4.6: e6022.

                                                                                                                          DOI: 10.1371/journal.pone.0006022Save Citation »Export Citation »E-mail Citation »

                                                                                                                          The authors perform a principal component analysis of the rankings produced by thirty-nine existing measures of scholarly impact: results indicate that the notion of scientific impact is a multidimensional construct that cannot be adequately measured by any single indicator.

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                                                                                                                          • Ding, Y., E. Yan, A. Frazho, and J. Caverlee. 2009. PageRank for ranking authors in co-citation networks. Journal of the American Society for Information Science and Technology 60.11: 2229–2243.

                                                                                                                            DOI: 10.1002/asi.21171Save Citation »Export Citation »E-mail Citation »

                                                                                                                            The authors test the relationship between different ranking measures like PageRanks, h-index, citation ranking, and centrality measures on 108 most influential authors’ co-citation network.

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                                                                                                                            • Fiala, D., F. Rousselot, and K. Ježek. 2008. PageRank for bibliographic networks. Scientometrics 76.1: 135–158.

                                                                                                                              DOI: 10.1007/s11192-007-1908-4Save Citation »Export Citation »E-mail Citation »

                                                                                                                              In this paper, the authors present several modifications of the classical PageRank formula adapted for bibliographic networks. These versions of PageRank take into account not only the citation but also the co-authorship graph.

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                                                                                                                              • Palla, G., G. Tibély, E. Mones, P. Pollner, and T. Vicsek. 2015. Hierarchical networks of scientific journals. Palgrave Communications 1:1–9.

                                                                                                                                DOI: 10.1057/palcomms.2015.16Save Citation »Export Citation »E-mail Citation »

                                                                                                                                The authors combine nested and flow hierarchy to organize journals into multiple hierarchies based on citation data.

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                                                                                                                                • Walker, D., H. Xie, K. K. Yan, and S. Maslov. 2007. Ranking scientific publications using a simple model of network traffic. Journal of Statistical Mechanics: Theory and Experiment P06010.

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                                                                                                                                  The paper modifies Google’s PageRank algorithm to weight results according to the age of publications. The output of this algorithm, called CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information.

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                                                                                                                                  • Yan, E., Y. Ding, and C. R. Sugimoto. 2011. P-Rank: An indicator measuring prestige in heterogeneous scholarly networks. Journal of the American Society for Information Science and Technology 62.3: 467–477.

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                                                                                                                                    This study proposes a new infometric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors.

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                                                                                                                                    Macro Structures

                                                                                                                                    A highly promising and useful area of research brings together the modeling expertise of physicists with statistical advancement in the study of multilevel dependencies. Starting from some of the properties suggested by the seminal work of de Solla Price (see Classic Works) some scholars advanced the hypothesis that large scientific networks (mainly constructed via co-authorship and citations) are characterized by a power law degree distribution, with few hubs connecting otherwise disconnected clusters and therefore producing the phenomenon of small worlds. These networks are also called scale free. Newman 2001 and Barabasi, et al. 2002 define and initiate the study of scale-free networks with power law distributions. Their approaches were able to identify important properties like the preferential attachment law, and have been replicated and extended in various studies. Wagnera and Leydesdorff 2005 explains the growth of international co-authorships with the organizing principle of preferential attachment; Yan, et al. 2010 and Liu and Xia 2015 observe, respectively, the small-world structure of the collaboration patterns of library and information science (LIS) in China and the interdisciplinary field of “evolution of cooperation.” Pütsch 2003 and Smolinsky, et al. 2015 model small-world networks for co-authorship and citation networks. Another macro approach to the study of complex networks adopts multilevel analysis, which combines single-level horizontal network dependencies (one-mode) with hierarchical group dependencies (multilevel and two-mode). Methods are now available for combining network data with group affiliations, as well as network data stemming from relations at various levels of analysis. Wang, et al. 2013 proposes a general formulation of a multilevel network structure and extends exponential random graph models (ERGMs) to multilevel networks, using the empirical case of cancer researchers in France first analyzed by Lazega, et al. 2008. Bellotti 2012 and Lopaciuk-Gonczaryk 2016 extend this line of analysis on two case studies: Italian physicists and Polish researchers.

                                                                                                                                    • Barabasi, A. L., H. Jeonga, Z. Néda, E. Ravasz, A. Schubertd, and T. Vicsek. 2002. Evolution of the social network of scientific collaborations. Physica A 311:590–614.

                                                                                                                                      DOI: 10.1016/S0378-4371(02)00736-7Save Citation »Export Citation »E-mail Citation »

                                                                                                                                      The article models the tendency toward cumulative advantage in co-authorship networks using the disciplines of mathematics and neuroscience as case studies. Results indicate that the networks are scale-free, and that their evolution is governed by preferential attachment law.

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                                                                                                                                      • Bellotti, E. 2012. Getting funded. Multi-level network of physicists in Italy. Social Networks 34.2: 215–229.

                                                                                                                                        DOI: 10.1016/j.socnet.2011.12.002Save Citation »Export Citation »E-mail Citation »

                                                                                                                                        The paper extends the structural approach of Lazega, et al. 2008 and analyzes the local system of public funding to physics in Italy using bipartite networks.

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                                                                                                                                        • Lazega, E., M. T. Jourda, L. Mounier, and R. Stofer. 2008. Catching up with big fish in the big pond? Multi-level network analysis through linked design. Social Networks 30:157–176.

                                                                                                                                          DOI: 10.1016/j.socnet.2008.02.001Save Citation »Export Citation »E-mail Citation »

                                                                                                                                          The study explores multi-level networks of superposed and partially connected interdependencies between researchers and organizations of the “elite” of French cancer researchers in 1999.

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                                                                                                                                          • Liu, P., and H. Xia. 2015. Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics 103.1: 101–134.

                                                                                                                                            DOI: 10.1007/s11192-014-1525-ySave Citation »Export Citation »E-mail Citation »

                                                                                                                                            The structure and evolution of co-authorship networks of the interdisciplinary field of “evolution of cooperation” are analyzed: results show that the development of this field is characterized by the growth of a giant component that has gradually evolved from a small cluster, to a structure of “chained-communities,” to a small-world structure.

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                                                                                                                                            • Lopaciuk-Gonczaryk, B. 2016. Collaboration strategies for publishing articles in international journals—A study of Polish scientists in economics. Social Networks 44:50–63.

                                                                                                                                              DOI: 10.1016/j.socnet.2015.07.001Save Citation »Export Citation »E-mail Citation »

                                                                                                                                              Case study of the co-authorship network of Polish researchers in economics that applies Lazega’s and Bellotti’s multilevel network methods and cluster analysis to identify different collaboration strategies.

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                                                                                                                                              • Newman, M. E. J. 2001. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America 98.2: 404–409.

                                                                                                                                                DOI: 10.1073/pnas.98.2.404Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                The small-world model describes a network with short paths between any two vertex and small dense clusters. These properties are measured on the co-authorship network of scientific publications in biomedical research, physics, and computer science.

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                                                                                                                                                • Pütsch, F. 2003. Analysis and modeling of science collaboration networks. Advances in Complex Systems 6:477.

                                                                                                                                                  DOI: 10.1142/S0219525903001043Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                  The study develops a model for the simulation of discontiguous small-world networks and tests it on co-authorship networks.

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                                                                                                                                                  • Smolinsky, L., A. Lercher, and A. McDaniel. 2015. Testing theories of preferential attachment in random networks of citations. Journal of the Association for Information Science and Technology 66.10: 2132–2145.

                                                                                                                                                    DOI: 10.1002/asi.23312Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                    The article examines stochastic models of the accumulation of citations characterized by growth, as in the introduction of new articles, and preferential attachment, which describes how established articles accumulate new citations.

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                                                                                                                                                    • Wagnera, C. S., and L. Leydesdorff. 2005. Network structure, self-organization, and the growth of international collaboration in science. Research Policy 34.10: 1608–1618.

                                                                                                                                                      DOI: 10.1016/j.respol.2005.08.002Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                      Applying tools from network analysis, the paper shows that the growth of international co-authorships can be explained based on the organizing principle of preferential attachment, although the attachment mechanism deviates from an ideal power-law.

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                                                                                                                                                      • Wang, P., G. Robins, P. Pattison, and E. Lazega. 2013. Exponential random graph models for multilevel networks. Social Networks 35.1: 96–115.

                                                                                                                                                        DOI: 10.1016/j.socnet.2013.01.004Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                        The article presents a general formulation of a multilevel network structure and extends exponential random graph models (ERGMs) to multilevel networks. The proposed models are applied to the collaboration network of French cancer research elites published in Lazega, et al. 2008.

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                                                                                                                                                        • Yan, E., Y. Ding, and Q. Zhu. 2010. Mapping library and information science in China: A co-authorship network analysis. Scientometrics 83:115–131.

                                                                                                                                                          DOI: 10.1007/s11192-009-0027-9Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                          The study analyzes the small-world network, with a scale-free character, of the collaboration patterns of library and information science (LIS) in China. It also finds that centrality rankings in co-authorship are highly correlated with citation rankings.

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                                                                                                                                                          Modeling

                                                                                                                                                          Exponential random graph models (ERGMs) are statistical models for social networks in which the possible ties among nodes are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the models.

                                                                                                                                                          • Gondal, N. 2011. The local and global structure of knowledge production in an emergent research field: An exponential random graph analysis. Social Networks 33:20–30.

                                                                                                                                                            DOI: 10.1016/j.socnet.2010.09.001Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                            Case study of publications pertaining to the rise in the employment of women in export production zones (EPZs) in South and East Asia using citation analysis and ERGM models.

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                                                                                                                                                            • Ferligoj, A., L. Kronegger, F. Mali, T. A. B. Snijders, and P. Doreian. 2015. Scientific collaboration dynamics in a national scientific system. Scientometrics 104:985–1012.

                                                                                                                                                              DOI: 10.1007/s11192-015-1585-7Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                              The paper combines the approaches of the small-world model and the mechanism of preferential attachment to examine the collaboration structures and dynamics of the co-authorship network of Slovenian researchers. The two approaches are compared using Stochastic-actor-based modeling of co-authorship network dynamics.

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