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Betweenness and diversity in journal citation networks as measures of interdisciplinarity--A tribute to Eugene Garfield

Published: 01 February 2018 Publication History

Abstract

Journals were central to Eugene Garfield's research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science and Scopus. In addition to providing a basis for disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines; the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary--that is, non-disciplined--variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion or differentiation of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed; differences among (sub)sets can be tested for statistical significance. In the appendix, a general-purpose routine for measuring diversity in networks is provided.

References

[1]
Abbasi, A., Hossain, L., & Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6 (3), 403-412.
[2]
Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient. Journal of the American Society for Information Science and Technology, 54 (6), 550-560.
[3]
Bensman, S. J. (2007). Garfield and the impact factor. Annual Review of Information Science and Technology, 41 (1), 93-155.
[4]
Bernal, J. D. (1939). The social function of science . London: Routledge & Kegan Paul, Ltd.
[5]
Boschma, R. (2005). Proximity and innovation: a critical assessment. Regional Studies, 39 (1), 61-74.
[6]
Boyack, K. W., Patek, M., Ungar, L. H., Yoon, P., & Klavans, R. (2014). Classification of individual articles from all of science by research level. Journal of Informetrics, 8 (1), 1-12.
[7]
Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25 (2), 163-177.
[8]
Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30 (2), 136-145.
[9]
Burt, R. S. (2001). Structural holes versus network closure as social capital. In N. Lin, K. Cook, & R. S. Burt (Eds.), Social capital: Theory and research (pp. 31-56). New Brunswick NJ: Transaction Publishers.
[10]
Cassi, L., Champeimont, R., Mescheba, W., & de Turckheim, É. (2017). Analysing institutions interdisciplinarity by extensive use of Rao-Stirling diversity index. PLoS ONE, 12 (1), e0170296.
[11]
Cassi, L., Mescheba, W., & De Turckheim, E. (2014). How to evaluate the degree of interdisciplinarity of an institution? Scientometrics, 101 (3), 1871-1895.
[12]
Chiu, C.-H., & Chao, A. (2014). Distance-based functional diversity measures and their decomposition: A framework based on hill numbers. PLoS ONE, 9 (7), e100014.
[13]
de Nooy, W., Mrvar, A., & Batgelj, V. (2011). Exploratory social network analysis with Pajek (2nd ed.). New York, NY: Cambridge University Press.
[14]
Flom, P. L., Friedman, S. R., Strauss, S., & Neaigus, A. (2004). A new measure of linkage between two subnetworks. Connections, 26 (1), 62-70.
[15]
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40 (1), 35-41.
[16]
Freeman, L. C. (1978/1979). Centrality in social networks: Conceptual clarification. Social Networks, 1 , 215-239.
[17]
Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13 (2), 141-154.
[18]
Garfield, E. (1971). The mystery of the transposed journal lists--wherein Bradford's law of scattering is generalized according to Garfield's law of concentration. Current Contents, 3 (33), 5-6.
[19]
Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178 (4060), 471-479.
[20]
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: the dynamics of science and research in contemporary societies . London: Sage.
[21]
Guns, R., & Rousseau, R. (2015). Unnormalized and normalized forms of gefura measures in directed and undirected networks. Frontiers of Information Technology & Electronic Engineering, 16 (4), 311-320.
[22]
Izsák, J., Papp, L. (1995). Application of the quadratic entropy indices for diversity studies of drosophilid assemblages. Environmental and Ecological Statistics, 2 (3), 213-224.
[23]
Jaffe, A. B. (1989). Characterizing the "technological position" of firms, with application to quantifying technological opportunity and research spillovers. Research Policy, 18 (2), 87-97.
[24]
Jost, L. (2006). Entropy and diversity. Oikos, 113 (2), 363-375.
[25]
Klein, J. T. (2010). Typologies of Interdisciplinarity. In R. Frodeman, J. T. Klein, & R. Pacheco (Eds.), The Oxford handbook of interdisciplinarity (pp. 21-34). Oxford: Oxford University Press.
[26]
Leydesdorff, L. (2006). Can scientific journals be classified in terms of aggregated journal-journal citation relations using the journal citation reports? Journal of the American Society for Information Science and Technology, 57 (5), 601-613.
[27]
Leydesdorff, L. (2015). Can technology life-cycles be indicated by diversity in patent classifications? The crucial role of variety. Scientometrics, 105 (3), 1441-1451.
[28]
Leydesdorff, L., & Bihui, J. (2005). Mapping the chinese science citation database in terms of aggregated journal-journal citation relations. [Article]. Journal of the American Society for Information Science and Technology, 56 (14), 1469-1479.
[29]
Leydesdorff, L., & Bornmann, L. (2016). The operationalization of "Fields" as WoS subject categories (WCs) in evaluative bibliometrics: The cases of "Library and Information Science" and "Science & Technology Studies". Journal of the Association for Information Science and Technology, 67 (3), 707-714.
[30]
Leydesdorff, L., Bornmann, L., & Wagner, C. S. (2017). Generating clustered journal maps: An automated system for hierarchical classification. Scientometrics, 110 (3), 1601-1614.
[31]
Leydesdorff, L., & Goldstone, R. L. (2014). Interdisciplinarity at the Journal and Specialty Level: The changing knowledge bases of the journal Cognitive Science. Journal of the Association for Information Science and Technology, 65 (1), 164-177.
[32]
Leydesdorff, L., & Nerghes, A. (2017). Co-word maps and topic modeling: A comparison from a user's perspective. Journal of the Association for Information Science and Technology, 68 (4), 1024-1035.
[33]
Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics, 5 (1), 87-100.
[34]
Leydesdorff, L., & Schank, T. (2008). Dynamic animations of journal maps: Indicators of structural change and interdisciplinary developments. Journal of the American Society for Information Science and Technology, 59 (11), 1810-1818.
[35]
MacCallum, C. J. (2006). ONE for all: The next step for PLoS. PLoS Biology, 4 (11), e401.
[36]
MacCallum, C. J. (2011). PLOS BIOLOGY-Editorial-Why ONE Is More Than 5. PLoS-Biology, 9 (12), 2457.
[37]
Mutz, R., Bornmann, L., & Daniel, H.-D. (2015). Cross-disciplinary research: What configurations of fields of science are found in grant proposals today? Research Evaluation, 24 (1), 30-36.
[38]
Narin, F., Carpenter, M., & Berlt, N. C. (1972). Interrelationships of scientific journals. Journal of the American Society for Information Science, 23, 323-331.
[39]
Neurath, O. (1932/1933). Protokollsätze. Erkenntnis, 3 , 204-214.
[40]
Newman, M. E. (2004). Analysis of weighted networks. Physical Review E, 70 (5), 056131.
[41]
Nichols, L. G. (2014). A topic model approach to measuring interdisciplinarity at the National Science Foundation. Scientometrics, 100 (3), 741-754.
[42]
Nijssen, D., Rousseau, R., & Van Hecke, P. (1998). The Lorenz curve: A graphical representation of evenness. Coenoses, 13 (1), 33-38.
[43]
OECD (1972). Interdisciplinarity: Problems of Teaching and Research . In L. Apostel, G. Berger, A. Briggs & G. Michaud (Eds.). Paris: OECD/Centre for Educational Research and Innovation.
[44]
Porter, A. L., Cohen, A. S., David Roessner, J., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72 (1), 117-147.
[45]
Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81 (3), 719-745.
[46]
Porter, A. L., Roessner, J. D., Cohen, A. S., & Perreault, M. (2006). Interdisciplinary research: meaning, metrics and nurture. Research Evaluation, 15 (3), 187-195.
[47]
Porter, A. L., Roessner, D. J., & Heberger, A. E. (2008). How interdisciplinary is a given body of research? Research Evaluation, 17 (4), 273-282.
[48]
Pudovkin, A. I., & Garfield, E. (2002). Algorithmic procedure for finding semantically related journals. Journal of the American Society for Information Science and Technology, 53 (13), 1113-1119.
[49]
Rafols, I. (2014). Knowledge integration and diffusion: Measures and mapping of diversity and coherence. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact (pp. 169-190). Cham, Heidelberg: Springer.
[50]
Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60 (9), 1823-1835.
[51]
Rafols, I., Leydesdorff, L., O'Hare, A., Nightingale, P., & Stirling, A. (2012). How journal rankings can suppress interdisciplinary research: A comparison between innovation studies and business management. Research Policy, 41 (7), 1262-1282.
[52]
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82 (2), 263-287.
[53]
Rao, C. R. (1982). Diversity: Its measurement, decomposition, apportionment and analysis. Sankhy : The Indian Journal of Statistics, Series A, 44 (1), 1-22.
[54]
Ricotta, C., Szeidl, L. (2006). Towards a unifying approach to diversity measures: bridging the gap between the Shannon entropy and Rao's quadratic index. Theoretical Population Biology, 70 (3), 237-243.
[55]
Rousseau, R., & Zhang, L. (2008). Betweenness centrality and Q-measures in directed valued networks. Scientometrics, 75 (3), 575-590.
[56]
Rousseau, R., Zhang, L., Hu, X. (2017, in preparation). Knowledge integration. In W. Glänzel, H. Moed, U. Schmoch M. Thelwall (Eds.), Springer handbook of science and technology indicators . Berlin: Springer.
[57]
Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval . Auckland: McGraw-Hill.
[58]
Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106 (6), 467-482.
[59]
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society, Interface, 4 (15), 707-719.
[60]
Stokols, D., Fuqua, J., Gress, J., Harvey, R., Phillips, K., Baezconde-Garbanati, L., et al. (2003). Evaluating transdisciplinary science. Nicotine & Tobacco Research, 5, S21-S39.
[61]
Theil, H. (1972). Statistical decomposition analysis . Amsterdam/London: North-Holland.
[62]
Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). A typical combinations and scientific impact. Science, 342 (6157), 468-472.
[63]
Van den Besselaar, P., & Leydesdorff, L. (1996). Mapping change in scientific specialties: A scientometric reconstruction of the development of artificial intelligence. Journal of the American Society for Information Science, 47 (6), 415-436.
[64]
Van den Daele, W., Krohn, W., & Weingart, P. (Eds.). (1979). Geplante Forschung: Vergleichende Studien über den Einfluss politischer Programme auf die Wissenschaftsentwicklung . Suhrkamp: Frankfurt a.M.
[65]
van den Daele, W., & Weingart, P. (1975). Resistenz und Rezeptivität der Wissenschaft-zu den Entstehungsbedingungen neuer Disziplinen durch wissenschaftliche und politische Steuerung. Zeitschrift fuer Soziologie, 4 (2), 146-164.
[66]
van Noorden, R. (2015). Interdisciplinary research by the numbers: an analysis reveals the extent and impact of research that bridges disciplines. Nature, 525 (7569), 306-308.
[67]
Wagner, C. S., Horlings, E., Whetsell, T. A., Mattsson, P., & Nordqvist, K. (2015). Do Nobel laureates create prize-winning networks? An analysis of collaborative research in physiology or medicine. PLoS ONE, 10 (7), e0134164.
[68]
Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5 (1), 14-26.
[69]
Waltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63 (12), 2378-2392.
[70]
Whitley, R. D. (1984). The intellectual and social organization of the sciences . Oxford: Oxford University Press.
[71]
Yan, E., Ding, Y., Cronin, B., & Leydesdorff, L. (2013). A bird's-eye view of scientific trading: Dependency relations among fields of science. Journal of Informetrics, 7 (2), 249-264.
[72]
Zhang, L., Rousseau, R., & Glänzel, W. (2016). Diversity of references as an indicator for interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of the American Society for Information Science and Technology, 67 (5), 1257-1265.

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cover image Scientometrics
Scientometrics  Volume 114, Issue 2
February 2018
392 pages

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2018

Author Tags

  1. Betweenness
  2. Diversity
  3. Granularity
  4. Interdisciplinarity
  5. Journal

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