, Volume 112, Issue 1, pp 91–109 | Cite as

Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization

  • Jiming Hu
  • Yin Zhang


Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.


Big Data research Interdisciplinary collaboration Network structure and patterns Visualization 



This study is supported by China Postdoctoral Science Foundation Special Funded Project (No. 2016T90736), China Postdoctoral Science Foundation Funded Project (No. 2015M572202), Wuhan University Initiative Scientific Research Project (No. 2015-79), National Natural Science Foundation of China Funded Project (No. 71303178), and Kent State University 2014 Postdoctoral Program for the Smart Big Data project.


  1. Agrawal, D., & Chawla, S. (2015). The Big Data landscape: Hurdles and opportunities. In W. Chu, S. Kikuchi & S. Bhalla (Eds.), Databases in networked information systems (pp. 1–11).Google Scholar
  2. Al-Jarrah, O. Y., Yoo, P. D., Muhaidat, S., Karagiannidis, G. K., & Taha, K. (2015). Efficient machine learning for Big Data: A review. Big Data Research, 2(3), 87–93.CrossRefGoogle Scholar
  3. Bardi, M., Zhou, X., Li, S., & Lin, F. (2014). Big Data security and privacy: A review. China Communications, 11(2), 135–145.Google Scholar
  4. Birnbaum, P. H. (1981). Academic interdisciplinary research: Characteristics of successful projects. Journal of the Society of Research Administrators, 13, 5–16.Google Scholar
  5. Bjurström, A., & Polk, M. (2011). Climate change and interdisciplinarity: A co-citation analysis of IPCC third assessment report. Scientometrics, 87, 525–550.CrossRefGoogle Scholar
  6. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 8(10), P10008.CrossRefGoogle Scholar
  7. Boerner, K. (2011). Plug-and-play macroscopes. Communications of the ACM, 54(3), 60–69.CrossRefGoogle Scholar
  8. Casado, R., & Younas, M. (2015). Emerging trends and technologies in Big Data processing. Concurrency and Computation-Practice and Experience, 27(8), 2078–2091.CrossRefGoogle Scholar
  9. Catala-Lopez, F., Alonso-Arroyo, A., Aleixandre-Benavent, R., Ridao, M., Bolanos, M., Garcia-Altes, A., & Peiró, S. (2012). Coauthorship and institutional collaborations on cost-effectiveness analyses: A systematic network analysis. Plos One, 7(5), e38012. doi: 10.1371/journal.pone.0038012.Google Scholar
  10. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From Big Data to big impact. MIS Quarterly, 36(4), 1165–1188.Google Scholar
  11. Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A survey. Mobile Networks and Applications, 19(2), 171–209.CrossRefGoogle Scholar
  12. Chen, C. L. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347.MathSciNetCrossRefGoogle Scholar
  13. Chi, R. B., & Young, J. (2013). The interdisciplinary structure of research on intercultural relations: A co-citation network analysis study. Scientometrics, 96(1), 147–171.CrossRefGoogle Scholar
  14. Clarke, R. (2016). Big data, big risks. Information Systems Journal, 26(1), 77–90.CrossRefGoogle Scholar
  15. Coulter, N., Monarch, I., & Konda, S. (1998). Software engineering as seen through its research literature: A study in co-word analysis. Journal of the American Society for Information Science, 49(13), 1206–1223.CrossRefGoogle Scholar
  16. De Mauro, A., Greco, M., & Grimaldi, M. (2014). What is Big Data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104.Google Scholar
  17. Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37(6), 817–842.CrossRefzbMATHGoogle Scholar
  18. Doreian, P., Lloyd, P., & Mrvar, A. (2013). Partitioning large signed two-mode networks: Problems and prospects. Social Networks, 35(2), 178–203.CrossRefGoogle Scholar
  19. Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., et al. (2015). Big Data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), 1523–1545.CrossRefGoogle Scholar
  20. Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable Big Data: A survey. Computer Science Review, 17, 70–81.MathSciNetCrossRefGoogle Scholar
  21. Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904.CrossRefGoogle Scholar
  22. Fang, H., Zhang, Z., Wang, C. J., Daneshmand, M., Wang, C., & Wang, H. (2015). A survey of Big Data research. IEEE Network, 29(5), 6–9.CrossRefGoogle Scholar
  23. Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.MathSciNetCrossRefGoogle Scholar
  24. Gil, D., & Song, I. (2016). Modeling and Management of Big Data: Challenges and opportunities. Future Generation Computer Systems-The International Journal of escience, 63, 96–99.CrossRefGoogle Scholar
  25. Goes, P. B. (2014). Big Data and IS research. MIS Quarterly, 38(3), III–VIII.Google Scholar
  26. Grauwin, S., & Jensen, P. (2011). Mapping scientific institutions. Scientometrics, 89(3), 943–954.CrossRefGoogle Scholar
  27. Hilbert, M. (2016). Big Data for development: A review of promises and challenges. Development Policy Review, 34(1), 135–174.CrossRefGoogle Scholar
  28. Hu, C.-P., Hu, J.-M., Gao, Y., & Zhang, Y.-K. (2011). A journal co-citation analysis of library and information science in China. Scientometrics, 86(3), 657–670.CrossRefGoogle Scholar
  29. Jacobs, J. A., & Frickel, S. (2009). Interdisciplinarity: A critical assessment. Annual Review of Sociology, 35, 43–65.CrossRefGoogle Scholar
  30. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in Big Data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573.CrossRefGoogle Scholar
  31. Khan, G. F., Moon, J., & Park, H. W. (2011). Network of the core: Mapping and visualizing the core of scientific domains. Scientometrics, 89(3), 759–779.CrossRefGoogle Scholar
  32. Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, W. K. M., Alam, M., et al. (2014). Big data: Survey, technologies, opportunities, and challenges. The Scientific World Journal. doi: 10.1155/2014/712826.Google Scholar
  33. Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data and Society, 1(1), 1–12. doi: 10.1177/2053951714528481.CrossRefGoogle Scholar
  34. Klein, J. T. (1990). Interdisciplinarity/history, theory, and practice. Detroit: Wayne State University Press.Google Scholar
  35. Klein, J. T. (2000). Interdisciplinarity and complexity: An evolving relationship. Emergence: Complexity and Organization, 6(1-2), 2–10.Google Scholar
  36. Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.CrossRefGoogle Scholar
  37. Leydesdorff, L., de Moya-Anegon, F., & Guerrero-Bote, V. P. (2015). Journal maps, interactive overlays, and the measurement of interdisciplinarity on the basis of scopus data (1996–2012). Journal of the Association for Information Science and Technology, 66(5), 1001–1016.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. Leydesdorff, L., Rafols, I., & Chen, C. (2013). Interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal-journal citations. Journal of the American Society for Information Science and Technology, 64(12), 2573–2586.CrossRefGoogle Scholar
  40. Li, K., Wu, H., & Li, Z. (2015). Big data cloud and the frontier of computer science and technology. Concurrency and Computation-Practice and Experience, 27(18), 5719–5721.CrossRefGoogle Scholar
  41. Liu, Z., & Wang, C. Z. (2005). Mapping interdisciplinarity in demography: A journal network analysis. Journal of Information Science, 31(4), 308–316.CrossRefGoogle Scholar
  42. Offroy, M., & Duponchel, L. (2016). Topological data analysis: A promising Big Data exploration tool in biology, analytical chemistry and physical chemistry. Analytica Chimica Acta, 910, 1–11.CrossRefGoogle Scholar
  43. Olsson, N. O. E., & Bull-Berg, H. (2015). Use of Big Data in project evaluations. International Journal of Managing Projects in Business, 8(3), 491–512.CrossRefGoogle Scholar
  44. Qin, J., Lancaster, F. W., & Allen, B. (1997). Types and levels of collaboration in interdisciplinary research in the sciences. Journal of the American Society for Information Science, 48(10), 893–916.CrossRefGoogle Scholar
  45. Rafols, I., & Meyer, M. (2007). Diversity measures and network centralities as indicators of interdisciplinarity: Case studies in bionanoscience. In Proceedings of ISSI 2007: 11th international conference of the international society for scientometrics and informetrics (Vols. I and II, pp. 631–642).Google Scholar
  46. Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287.CrossRefGoogle Scholar
  47. Rosvall, M., & Bergstrom, C. T. (2010). Mapping change in large networks. PLoS ONE, 5(1), e8694.CrossRefGoogle Scholar
  48. Savitz, E. (2012a, October 22). Gartner: 10 Critical tech trends for the next five years. Forbes. Retrieved from
  49. Savitz, E. (2012b, October 23). Gartner: Top 10 strategic technology trends for 2013. Forbes. Retrieved from
  50. Singh, V. K., Banshal, S. K., Singhal, K., & Uddin, A. (2015). Scientometric mapping of research on ‘Big Data’. Scientometrics, 105(2), 727–741.CrossRefGoogle Scholar
  51. Small, H. (2010). Maps of science as interdisciplinary discourse: Co-citation contexts and the role of analogy. Scientometrics, 83(3), 835–849.CrossRefGoogle Scholar
  52. Small, H., & Griffith, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies, 4(1), 17–40.CrossRefGoogle Scholar
  53. Szalay, A. S. (2011). Extreme Data-Intensive Scientific Computing. Computing in Science and Engineering, 13(6), 34–41.CrossRefGoogle Scholar
  54. Taskin, Z., & Aydinoglu, A. U. (2015). Collaborative interdisciplinary astrobiology research: A bibliometric study of the NASA Astrobiology Institute. Scientometrics, 103(3), 1003–1022.CrossRefGoogle Scholar
  55. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.CrossRefGoogle Scholar
  56. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘Big Data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.CrossRefGoogle Scholar
  57. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  58. Whitley, R. (2000). The intellectual and social organization of the sciences (2nd ed.). Oxford: Oxford University Press.Google Scholar
  59. Wu, L., Yuan, L., & You, J. (2015). Survey of large-scale data management systems for Big Data applications. Journal of Computer Science and Technology, 30(1), 163–183.CrossRefGoogle Scholar
  60. Yacioob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., et al. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231–1247.CrossRefGoogle Scholar
  61. Yan, E., Ding, Y., & Zhu, Q. (2010). Mapping library and information science in China: A coauthorship network analysis. Scientometrics, 83(1), 115–131.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.School of Information ManagementWuhan UniversityWuhanChina
  2. 2.School of Library and Information ScienceKent State UniversityKentUSA

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