Advertisement

Scientometrics

, Volume 114, Issue 3, pp 1011–1029 | Cite as

Role of interdisciplinarity in computer sciences: quantification, impact and life trajectory

  • Tanmoy Chakraborty
Article
  • 389 Downloads

Abstract

The tremendous advances in computer science in the last few decades have provided the platform to address and solve complex problems using interdisciplinary research. In this paper, we investigate how the extent of interdisciplinarity in computer science domain (which is further divided into 24 research fields) has changed over the last 50 years. To this end, we collect a massive bibliographic dataset with rich metadata information. We start with quantifying interdisciplinarity of a field in terms of the diversity of topics and citations. We then analyze the effect of interdisciplinary research on the scientific impact of individual fields and observe that highly disciplinary and highly interdisciplinary papers in general have a low scientific impact; remarkably those that are able to strike a balance between the two extremes eventually land up having the highest impact. Further, we study the reciprocity among fields through citation interactions and notice that links from one field to related and citation-intensive fields (fields producing large number of citations) are reciprocated heavily. A systematic analysis of the citation interactions reveals the life trajectory of a research field, which generally undergoes three phases—a growing phase, a matured phase and an interdisciplinary phase. The combination of metrics and empirical observations presented here provides general benchmarks for future studies of interdisciplinary research activities in other domains of science.

Keywords

Interdisciplinarity Computer science Reciprocity Life cycle 

References

  1. Carayol, N., & Thi, T. U. N. (2005). Why do academic scientists engage in interdisciplinary research? Research Evaluation, 14(1), 70–79.  https://doi.org/10.3152/147154405781776355. http://rev.oxfordjournals.org/content/14/1/70.abstract.
  2. Chakraborty, T., Ganguly, N. & Mukherjee, A. (2014a). Rising popularity of interdisciplinary research: An analysis of citation networks. In Sixth international conference on communication systems and networks (COMSNETS), Bangalore, India (pp. 1–6).Google Scholar
  3. Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N. & Mukherjee, A. (2014b). Towards a stratified learning approach to predict future citation counts. In ACM/IEEE-CS JCDL, Piscataway, NJ, USA (pp. 351–360).Google Scholar
  4. Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A. (2015a). On the categorization of scientific citation profiles in computer science. Communications of the ACM, 58(9), 82–90.  https://doi.org/10.1145/2701412.CrossRefGoogle Scholar
  5. Chakraborty, T., Kumar, S., Reddy, M.D., Kumar, S., Ganguly, N. & Mukherjee, A. (2013). Automatic classification and analysis of interdisciplinary fields in computer sciences. In SocialCom (pp. 180–187). IEEE. http://dblp.uni-trier.de/db/conf/socialcom/socialcom2013.html#0002KRKGM13.
  6. Chakraborty, T., Modani, N., Narayanam, R. & Nagar, S. (2015b). Discern: A diversified citation recommendation system for scientific queries. In 2015 IEEE 31st international conference on data engineering (pp. 555–566).  https://doi.org/10.1109/ICDE.2015.7113314.
  7. Chakraborty, T., Sikdar, S., Ganguly, N., & Mukherjee, A. (2014c). Citation interactions among computer science fields: A quantitative route to the rise and fall of scientific research. Social Network Analysis and Mining, 4(1), 187.CrossRefGoogle Scholar
  8. Chin, W. S., Juan, Y. C., Zhuang, Y., Wu, F., Tung, H. Y., Yu, T., et al. (2013). Effective string processing and matching for author disambiguation. In Proceedings of the 2013 KDD cup 2013 workshop, KDD Cup ’13 (pp. 7:1–7:9). New York, NY: ACM.  https://doi.org/10.1145/2517288.2517295.
  9. de Solla Price, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510–515.CrossRefGoogle Scholar
  10. Fu, T. Z. J., Song, Q., & Chiu, D. M. (2014). The academic social network. Scientometrics, 101(1), 203–239.  https://doi.org/10.1007/s11192-014-1356-x.CrossRefGoogle Scholar
  11. Garlaschelli, D., & Loffredo, M. I. (2004). Patterns of link reciprocity in directed networks. Physical Review Letters, 93, 268701.CrossRefGoogle Scholar
  12. Gingras, Y., Lariviere, V., Macaluso, B., & Robitaille, J. P. (2008). The effects of aging on researchers’ publication andcitation patterns. PLoS ONE, 3, e4048.  https://doi.org/10.1371/journal.pone.0004048.CrossRefGoogle Scholar
  13. Klein, J. (1990). Interdisciplinarity: History, theory, and practice. Detroit: Wayne State University Press.Google Scholar
  14. Larivire, V., & Gingras, Y. (2010). On the relationship between interdisciplinarity and scientific impact. JASIST, 61(1), 126–131.CrossRefGoogle Scholar
  15. Levitt, J. M., & Thelwall, M. (2008). Is multidisciplinary research more highly cited? A macrolevel study. JASIST, 59(12), 1973–1984. http://dblp.uni-trier.de/db/journals/jasis/jasis59.html#LevittT08.
  16. 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
  17. Li, C. L., Su, Y. C., Lin, T. W., Tsai, C. H., Chang, W. C., Huang, K. H., et al. (2013). Combination of feature engineering and ranking models for paper-author identification in kdd cup 2013. In Proceedings of the 2013 KDD cup 2013 workshop, KDD Cup ’13 (pp. 2:1–2:7). New York, NY: ACM.  https://doi.org/10.1145/2517288.2517290.
  18. Liu, J., Lei, K. H., Liu, J. Y., Wang, C., & Han, J. (2013). Ranking-based name matching for author disambiguation in bibliographic data. In Proceedings of the 2013 KDD cup 2013 workshop, KDD Cup ’13 (pp. 8:1–8:8). New York, NY: ACM.  https://doi.org/10.1145/2517288.2517296.
  19. Metzger, N., & Zare, R. N. (1999). Scince policy: Interdisciplinary research—From belief to reality. Science, 283(5402), 642–643.CrossRefGoogle Scholar
  20. Moed, H., De Bruin, R., & Van Leeuwen, T. (1995). New bibliometric tools for the assessment of national research performance: Database description, overview of indicators and first applications. Scientometrics, 33(3), 381–422.CrossRefGoogle Scholar
  21. Morillo, F., Bordons, M., & Gómez, I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51(1), 203–222.CrossRefGoogle Scholar
  22. Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the American Society for Information Science and Technology, 54(13), 1237–1249.  https://doi.org/10.1002/asi.10326.CrossRefGoogle Scholar
  23. Newman, M. (2001a). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102.CrossRefGoogle Scholar
  24. Newman, M. E. J. (2001b). The structure of scientific collaboration networks. PNAS, 98(2), 404–409.MathSciNetCrossRefMATHGoogle Scholar
  25. Pan, R. K., Sinha, S., Kaski, K., & Saramäki, J. (2012). The evolution of interdisciplinarity in physics research. Nature Scientific Reports, 2, 551.CrossRefGoogle Scholar
  26. Porter, A. L., & Chubin, D. E. (1985). An indicator of cross-disciplinary research. Scientometrics, 8(3–4), 161–176. http://dblp.uni-trier.de/db/journals/scientometrics/scientometrics8.html#PorterC85.
  27. Porter, A. L., Cohen, A. S., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147.CrossRefGoogle Scholar
  28. Pradhan, D., Paul, P. S., Maheswari, U., Nandi, S., & Chakraborty, T. (2017). \(C^3\)-index: A pagerank based multi-faceted metric for authors’ performance measurement. Scientometrics, 110(1), 253–273.  https://doi.org/10.1007/s11192-016-2168-y.CrossRefGoogle Scholar
  29. Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287.CrossRefGoogle Scholar
  30. Rinia, E., Van Leeuwen, T., Bruins, E., Van Vuren, H., & Van Raan, A. (2001). Citation delay in interdisciplinary knowledge exchange. Scientometrics, 51(1), 293–309.  https://doi.org/10.1023/A:1010589300829.CrossRefGoogle Scholar
  31. Rinia, E. J., van Leeuwen, T. N., Bruins, E. E. W., van Vuren, H. G., & van Raan, A. F. J. (2002). Measuring knowledge transfer between fields of science. Scientometrics, 54(3), 347–362.CrossRefGoogle Scholar
  32. Sayama, H., & Akaishi, J. (2012). Characterizing interdisciplinarity of researchers and research topics using web search engines. CoRR, arXiv:1201.3592.
  33. Schmidt, J. C. (2008). Towards a philosophy of interdisciplinarity. Poiesis & Praxis, 5(1), 53–69.  https://doi.org/10.1007/s10202-007-0037-8.CrossRefGoogle Scholar
  34. Schubert, A., & Braun, T. (1986). Relative indicators and relational charts for comparative assessment of publication output and citation impact. Scientometrics, 9(5–6), 281–291.CrossRefGoogle Scholar
  35. Singh, M., Chakraborty, T., Mukherjee, A., & Goyal, P. (2015). Confassist: A conflict resolution framework for assisting the categorization of computer science conferences. In ACM/IEEE-CS JCDL (pp. 257–258). New York: ACM.Google Scholar
  36. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B. J. P., et al. (2015). An overview of microsoft academic service (MAS) and applications. In Proceedings of the 24th international conference on World Wide Web, WWW ’15 companion (pp. 243–246). New York, NY: ACM.  https://doi.org/10.1145/2740908.2742839.
  37. Steele, T. W., & Stier, J. C. (2000). The impact of interdisciplinary research in the environmental sciences: A forestry case study. Journal of the Association for Information Science and Technology, 51(5), 476–484.  https://doi.org/10.1002/(SICI)1097-4571(2000)51:5%3c476::AID-ASI8%3e3.3.CO;2-7.Google Scholar
  38. Urata, H. (1990). Information flows among academic disciplines in Japan. Scientometrics, 18(3–4), 309–319.CrossRefGoogle Scholar
  39. Vrettas, G., & Sanderson, M. (2015). Conferences versus journals in computer science. Journal of the Association for Information Science and Technology, 66(12), 2674–2684.  https://doi.org/10.1002/asi.23349.CrossRefGoogle Scholar
  40. Wade, A. D., Wang, K., Sun, Y., & Gulli, A. (2016). Wsdm cup 2016: Entity ranking challenge. In Proceedings of the ninth ACM international conference on web search and data mining, WSDM ’16 (pp. 593–594). New York, NY: ACM.  https://doi.org/10.1145/2835776.2855119.
  41. 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: A review of the literature. Journal of Informetrics, 5(1), 14–26.CrossRefGoogle Scholar
  42. Wallace, M. L., Lariviére, V., & Gingras, Y. (2009). Modeling a century of citation distributions. Journal of Informetrics 3(4), 296–303. http://dblp.uni-trier.de/db/journals/joi/joi3.html#WallaceLG09.
  43. Wang, J., Thijs, B., & Glänzel, W. (2015). Interdisciplinarity and impact: Distinct effects of variety, balance, and disparity. PLoS ONE, 10(5), e0127298.  https://doi.org/10.1371/journal.pone.0127298.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.Indraprastha Institute of Information Technology, Delhi (IIIT-D)New DelhiIndia

Personalised recommendations