Skip to main content
Log in

Comparative study on structure and correlation among author co-occurrence networks in bibliometrics

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

This paper introduces author-level bibliometric co-occurrence network by discussing its history and contribution to the analysis of scholarly communication and intellectual structure. The difference among various author co-occurrence networks, which type of network shall be adapted in different situations, as well as the relationship among these networks, however, remain not explored. Five types of author co-occurrence networks were proposed: (1) co-authorship (CA); (2) author co-citation (ACC); (3) author bibliographic coupling (ABC); (4) words-based author coupling (WAC); (5) journals-based author coupling (JAC). Networks of 98 high impact authors from 30 journals indexed by 2011 version of Journal Citation Report-SSCI under the Information Science & Library Science category are constructed for study. Social network analysis and hierarchical cluster analysis are applied to identify sub-networks with results visualized by VOSviewer software. QAP test is used to find potential correlation among networks. Cluster analysis results show that all the five types of networks have the power for revealing intellectual structure of sciences but the revealed structures are different from each other. ABC identified more sub-structures than other types of network, followed by CA and ACC. WAC result is easily affected and JAC result is ambiguous. QAP test result shows that ABC network has the highest proximity with other types of networks while CA network has relatively lower proximity with other networks. This paper will provide a better comprehension of author interaction and contribute to cognitive application of author co-occurrence network analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Acedo, F. J., Barroso, C., Casanueva, C., & Galán, J. L. (2006). Co-authorship in management and organizational studies: an empirical and network analysis. Journal of Management Studies, 43(5), 957–983.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Borgatti, S., Everett, M., & Freeman, L. (2002). UCINET 6 for Windows: Software for social network analysis (Version 6.102). Harvard: Analytic Technologies.

    Google Scholar 

  • Boyack, K. W., & Klavans, R. (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.

    Article  Google Scholar 

  • Cabanac, G. (2011). Accuracy of inter-researcher similarity measures based on topical and social clues. Scientometrics, 87(3), 597–620.

    Article  Google Scholar 

  • Chen, L.-C., & Lien, Y.-H. (2011). Using author co-citation analysis to examine the intellectual structure of e-learning: A MIS perspective. Scientometrics, 89(3), 867–886.

    Article  Google Scholar 

  • Chen, C., Paul, R. J., & O’Keefe, B. (2001). Fitting the jigsaw of citation: Information visualization in domain analysis. Journal of the American Society for Information Science and Technology, 52(4), 315–330.

    Article  Google Scholar 

  • de Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek (Vol. 27). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • de Solla Price, D. J., & Beaver, D. (1966). Collaboration in an invisible college. American Psychologist, 21(11), 1011.

    Article  Google Scholar 

  • Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of informetrics, 5(1), 187–203.

    Article  Google Scholar 

  • Ding, Y., & Cronin, B. (2011). Popular and/or prestigious? Measures of scholarly esteem. Information Processing and Management, 47(1), 80–96.

    Article  Google Scholar 

  • Egghe, L., & Leydesdorff, L. (2009). The relation between Pearson’s correlation coefficient r and Salton’s cosine measure. Journal of the American Society for Information Science and Technology, 60(5), 1027–1036.

    Article  Google Scholar 

  • Eslami, H., Ebadi, A., & Schiffauerova, A. (2013). Effect of collaboration network structure on knowledge creation and technological performance: The case of biotechnology in Canada. Scientometrics, 97(1), 99–119.

    Article  Google Scholar 

  • Fox, M. F. (2008). Collaboration between science and social science: Issues, challenges, and opportunities. Research in Social Problems and Public Policy, 16, 17–30.

    Article  Google Scholar 

  • Garfield, E. (1972). Citation analysis as a tool in journal evaluation. American Association for the Advancement of Science.

  • Garfield, E. (1996). Significant scientific literature appears in a small core of journals: Scientist, Incorporated.

  • Garfield, E., & Merton, R. K. (1979). Citation indexing: Its theory and application in science, technology, and humanities (Vol. 8). New York: Wiley.

    Google Scholar 

  • Gazni, A., & Didegah, F. (2011). Investigating different types of research collaboration and citation impact: A case study of Harvard University’s publications. Scientometrics, 87(2), 251–265.

    Article  Google Scholar 

  • Groh, G., & Fuchs, C. (2011). Multi-modal social networks for modeling scientific fields. Scientometrics, 89(2), 569–590.

    Article  Google Scholar 

  • He, T. (2009). International scientific collaboration of China with the G7 countries. Scientometrics, 80(3), 571–582.

    Article  Google Scholar 

  • Hubert, L., & Schultz, J. (1976). Quadratic assignment as a general data analysis strategy. British Journal of Mathematical and Statistical Psychology, 29(2), 190–241.

  • Johnson, B., & Oppenheim, C. (2007). How socially connected are citers to those that they cite? Journal of Documentation, 63(5), 609–637.

  • Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.

    Article  Google Scholar 

  • Kim, S., & Cho, S. (2013). Characteristics of Korean personal names. Journal of the American Society for Information Science and Technology, 64(1), 86–95.

    Article  Google Scholar 

  • Kretschmer, H. (2004). Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the Web. Scientometrics, 60(3), 409–420.

    Article  Google Scholar 

  • Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

    Article  Google Scholar 

  • Leydesdorff, L., & Vaughan, L. (2006). Co-occurrence matrices and their applications in information science: Extending ACA to the Web environment. Journal of the American Society for Information Science and Technology, 57(12), 1616–1628.

    Article  Google Scholar 

  • Lin, W.-Y. C., & Huang, M.-H. (2012). The relationship between co-authorship, currency of references and author self-citations. Scientometrics, 90(2), 343–360.

    Article  Google Scholar 

  • Ma, R. (2012). Author bibliographic coupling analysis: A test based on a Chinese academic database. Journal of Informetrics, 6(4), 532–542.

    Article  Google Scholar 

  • McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.

    Article  Google Scholar 

  • Newman, M. E. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64 (1), 016131.

  • Newman, M. E. (2001b). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64 (1), 016132.

  • Newman, M. E. (2001c). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Newman, M. E. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5200–5205.

    Article  Google Scholar 

  • Ni, C., Sugimoto, C. R., & Cronin, B. (2013a). Visualizing and comparing four facets of scholarly communication: producers, artifacts, concepts, and gatekeepers. Scientometrics, 94 (3), 1161–1173.

  • Ni, C., Sugimoto, C. R., & Jiang, J. (2013b). Venue‐author‐coupling: A measure for identifying disciplines through author communities. Journal of the American Society for Information Science and Technology, 64 (2), 265–279.

  • Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of information Science, 28(6), 441–453.

    Article  Google Scholar 

  • Rousseau, R. (2010). Bibliographic coupling and co-citation as dual notions. A Festschrift in Honour of Peter Ingwersen, Special Volume of the e-Zine of the ISSI, 2010, 173–183.

    MathSciNet  Google Scholar 

  • Salton, G. (1989). Automatic text processing: The transformation, analysis, and retrieval of information by computer. Reading: Addison-Wesley.

    Google Scholar 

  • Schneider, J. W., Larsen, B., & Ingwersen, P. (2009). A comparative study of first and all-author co-citation counting, and two different matrix generation approaches applied for author co-citation analyses. Scientometrics, 80(1), 103–130.

    Article  Google Scholar 

  • Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.

    Article  Google Scholar 

  • Song, M., & Kim, S. (2013). Detecting the knowledge structure of bioinformatics by mining full-text collections. Scientometrics, 96(1), 183–201.

    Article  Google Scholar 

  • Sooryamoorthy, R. (2009). Do types of collaboration change citation? Collaboration and citation patterns of South African science publications. Scientometrics, 81(1), 177–193.

    Article  Google Scholar 

  • Strotmann, A., & Bleier, A. (2013). Author name co-mention analysis: Testing a poor man’s author co-citation analysis method. arXiv preprint arXiv:1309.5256.

  • Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). ArnetMiner: extraction and mining of academic social networks. Paper presented at the Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining.

  • Thijs, B., & Glänzel, W. (2010). A structural analysis of collaboration between European research institutes. Research Evaluation, 19(1), 55–65.

    Article  Google Scholar 

  • Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2005). A probabilistic similarity metric for medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), 140–158.

    Article  Google Scholar 

  • Van Eck, N. J., & Waltman, L. (2008). Appropriate similarity measures for author co-citation analysis. Journal of the American Society for Information Science and Technology, 59(10), 1653–1661.

    Article  Google Scholar 

  • Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

    Article  Google Scholar 

  • van Rijnsoever, F. J., Hessels, L. K., & Vandeberg, R. L. (2008). A resource-based view on the interactions of university researchers. Research Policy, 37(8), 1255–1266.

    Article  Google Scholar 

  • Wagner, C. S. (2005). Six case studies of international collaboration in science. Scientometrics, 62(1), 3–26.

    Article  Google Scholar 

  • Wallace, M. L., Larivière, V., & Gingras, Y. (2012). A small world of citations? The influence of collaboration networks on citation practices. PLoS One, 7(3), e33339.

    Article  Google Scholar 

  • White, H. D. (2003). Pathfinder networks and author cocitation analysis: A remapping of paradigmatic information scientists. Journal of the American Society for Information Science and Technology, 54(5), 423–434.

    Article  Google Scholar 

  • White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for information Science, 32(3), 163–171.

    Article  Google Scholar 

  • White, H. D., & Griffith, B. C. (1982). Authors as markers of intellectual space: Co-citation in studies of science, technology and society. Journal of Documentation, 38(4), 255–272.

    Article  Google Scholar 

  • White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972–1995. Journal of the American Society for Information Science, 49(4), 327–355.

    Google Scholar 

  • White, H. D., Wellman, B., & Nazer, N. (2004). Does citation reflect social structure?: Longitudinal evidence from the “Globenet” interdisciplinary research group. Journal of the American Society for Information Science and Technology, 55(2), 111–126.

    Article  Google Scholar 

  • Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.

    Article  Google Scholar 

  • Yan, E., & Ding, Y. (2012). Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Journal of the American Society for Information Science and Technology, 63(7), 1313–1326.

    Article  Google Scholar 

  • Zhao, D., & Strotmann, A. (2008a). Comparing all-author and first-author co-citation analyses of information science. Journal of Informetrics, 2(3), 229–239.

    Article  Google Scholar 

  • Zhao, D., & Strotmann, A. (2008b). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086.

    Article  Google Scholar 

  • Zhao, D., & Strotmann, A. (2008c). Information science during the first decade of the web: An enriched author cocitation analysis. Journal of the American Society for Information Science and Technology, 59(6), 916–937.

    Article  Google Scholar 

  • Zitt, M., Lelu, A., & Bassecoulard, E. (2011). Hybrid citation-word representations in science mapping: Portolan charts of research fields? Journal of the American Society for Information Science and Technology, 62(1), 19–39.

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by the Major Program of the National Social Science Foundation of China (Grant No. 11&ZD152) and Humanities and Social Sciences project of Wuhan University (Grant No. 2012GSP032).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ke Dong or Hou-Qiang Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, JP., Dong, K. & Yu, HQ. Comparative study on structure and correlation among author co-occurrence networks in bibliometrics. Scientometrics 101, 1345–1360 (2014). https://doi.org/10.1007/s11192-014-1315-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-014-1315-6

Keywords

Navigation