Advertisement

Scientometrics

, Volume 117, Issue 1, pp 85–103 | Cite as

Important institutions of interinstitutional scientific collaboration networks in materials science

  • Yang Li
  • Huajiao Li
  • Nairong Liu
  • Xueyong Liu
Article
First Online:
  • 251 Downloads

Abstract

Interinstitutional scientific collaboration plays an important role in knowledge production and scientific development. Together with the increasing scale of scientific collaboration, a few institutions that positively participate in interinstitutional scientific collaboration are important in collaboration networks. However, whether becoming an important institution in collaboration networks could be a contributing factor to research success and how these important institutions collaborate are still indistinct. In this paper, we identified the scientific institutions that possess the highest degree centrality as important institutions of an interinstitutional scientific collaboration network in materials science and examined their collaboration preferences utilizing several network measures. We first visualized the appearance of these important institutions that had the most positive collaborations in the interinstitutional scientific collaboration networks during the period of 2005–2015 and found an obvious scale-free feature in interinstitutional scientific collaboration networks. Then, we measured the advantages of being important in collaboration networks to research performance and found that positive interinstitutional collaborations can always bring both publication advantages and citation advantages. Finally, we identified two collaboration preferences of these important institutions in collaboration networks—one type of important institution represented by the Chinese Academy of Science plays an intermediary role between domestic institutions and foreign institutions with high betweenness centrality and a low clustering coefficient. This type of important institution has better performance in the number of publications. The other type of important institution represented by MIT tends to collaborate with similar institutions that have positive collaborations and possess a larger citation growth rate. Our finding can provide a better understanding of important institutions’ collaboration preferences and have significant reference for government policy and institutional collaboration strategies.

Keywords

Network Scientific collaboration Materials science Important institutions Collaboration preference 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This research is supported by grants from the National Natural Science Foundation of China (Grant No. 41701121). The authors would like to express their gratitude to Prof. Haizhong An, Dr. Xiangyun Gao and Dr. Shupei Huang who provided valuable suggestions, and AJE-American Journal Experts who provided professional suggestions about language usage, spelling, and grammar.

References

  1. Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: a correlation and regression analysis of performance measures and social network analysis measures. TEMEP Discussion Papers, 5, 594–607.Google Scholar
  2. Adams, J. (2012). Collaborations: The rise of research networks. Nature, 490, 335.CrossRefGoogle Scholar
  3. Asadi, S., Hussin, A. R. C., & Dahlan, H. M. (2017). Organizational research in the field of Green IT: A systematic literature review from 2007 to 2016. Telematics and Informatics, 34, 1191–1249.CrossRefGoogle Scholar
  4. Athen, M., Mondragón, R. J., & Vito, L. (2015). Anatomy of funded research in science. Proceedings of the National Academy of Sciences of the United States of America, 112, 14760.CrossRefGoogle Scholar
  5. Avkiran, N. K. (2013). An empirical investigation of the influence of collaboration in Finance on article impact. Scientometrics, 95, 911–925.CrossRefGoogle Scholar
  6. Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.MathSciNetCrossRefzbMATHGoogle Scholar
  7. Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2001). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3), 590–614.MathSciNetzbMATHGoogle Scholar
  8. Bhattacharyya, M., & Bandyopadhyay, S. (2015). Finding quasi core with simulated stacked neural networks. Information Sciences, 294, 1–14.MathSciNetCrossRefzbMATHGoogle Scholar
  9. Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23, 191–201.CrossRefGoogle Scholar
  10. Bornmann, L., & Daniel, H. D. (2005). Does the h-index for ranking of scientists really work? Scientometrics, 65, 391–392.CrossRefGoogle Scholar
  11. Breiger, R. L. (1974). The Duality of Persons and Groups. Social Forces, 53, 181–190.CrossRefGoogle Scholar
  12. Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and methods in social network analysis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  13. Çavuşoğlu, A., & Türker, İ. (2014). Patterns of collaboration in four scientific disciplines of the Turkish collaboration network. Physica A: Statistical Mechanics and its Applications, 413, 220–229.CrossRefGoogle Scholar
  14. Chang, H. W., & Huang, M. H. (2013). Prominent institutions in international collaboration network in astronomy and astrophysics. New York: Springer.Google Scholar
  15. Choi, S., Yang, S. W., & Han, W. P. (2015). The triple helix and international collaboration in science. Journal of the Association for Information Science and Technology, 66, 201–212.CrossRefGoogle Scholar
  16. Dorogovtsev, S. N., & Mendes, J. F. F. (2002). Evolution of networks. Advances in Physics, 51, 1079–1187.CrossRefGoogle Scholar
  17. Drożdż, S., Kulig, A., Kwapień, J., Niewiarowski, A., & Stanuszek, M. (2017). Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation. Journal of Informetrics, 11, 1114–1127.CrossRefGoogle Scholar
  18. Ebadi, A., & Schiffauerova, A. (2015). How to become an important player in scientific collaboration networks? Journal of Informetrics, 9, 809–825.CrossRefGoogle Scholar
  19. Egghe, L., & Rousseau, R. (1990). Introduction to Informetrics. Information Processing and Management, 28, 1–3.Google Scholar
  20. Fafchamps, M., Leij, M. J. V. D., & Goyal, S. (2010). Matching and network effects. Journal of the European Economic Association, 8, 203–231.CrossRefGoogle Scholar
  21. Freeman, L. C., Roeder, D., & Mulholland, R. R. (1980). Centrality in social networks: II. experimental result. Social Networks, 2, 119–141.CrossRefGoogle Scholar
  22. Fuchs, C. (2017). Sustainability and community networks. Telematics and Informatics, 34, 628–639.CrossRefGoogle Scholar
  23. Gazni, A., & Thelwall, M. (2016). The citation impact of collaboration between top institutions: A temporal analysis. Research Evaluation, 25, 219–229.CrossRefGoogle Scholar
  24. Ghosh, J., Kshitij, A., & Kadyan, S. (2015). Functional information characteristics of large-scale research collaboration: network measures and implications. Scientometrics, 102, 1207–1239.CrossRefGoogle Scholar
  25. Goh, K. I., Oh, E., Kahng, B., & Kim, D. (2003). Betweenness centrality correlation in social networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 67, 017101.CrossRefGoogle Scholar
  26. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102, 16569–16572.CrossRefzbMATHGoogle Scholar
  27. Hoekman, J., Frenken, K., & Tijssen, R. J. W. (2010). Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe. Research Policy, 39, 662–673.CrossRefGoogle Scholar
  28. Karlovčec, M., & Mladenić, D. (2015). Interdisciplinarity of scientific fields and its evolution based on graph of project collaboration and co-authoring. Scientometrics, 102, 433–454.CrossRefGoogle Scholar
  29. Kockelmans, J. J. (Ed.). (1979). Interdisciplinarity and higher education. State College: The Pennysylvania State Univ.Google Scholar
  30. Kronegger, L., Mali, F., Anu, X., Ferligoj, K., & Doreian, P. (2015). Classifying scientific disciplines in Slovenia: A study of the evolution of collaboration structures. Journal of the Association for Information Science and Technology, 66, 321–339.CrossRefGoogle Scholar
  31. Latora, V., Nicosia, V., & Panzarasa, P. (2013). Social cohesion, structural holes, and a tale of two measures. Journal of Statistical Physics, 151, 745–764.MathSciNetCrossRefzbMATHGoogle Scholar
  32. Li, H. J., An, H. Z., Huang, J. C., Gao, X. Y., & Shi, Y. L. (2014). Correlation of the holding behaviour of the holding-based network of Chinese fund management companies based on the node topological characteristics. Acta Physica Sinica, 63, 048901–048913.Google Scholar
  33. Li, J., & Li, Y. (2015). Patterns and evolution of coauthorship in China’s humanities and social sciences. Scientometrics, 102, 1997–2010.CrossRefGoogle Scholar
  34. Ma, A., & Mondragón, R. J. (2015). Rich-cores in networks. PLoS ONE, 10, e0119678.CrossRefGoogle Scholar
  35. Mattsson, P., Laget, P., Nilsson, A., & Sundberg, C. J. (2008). Intra-EU vs. extra-EU scientific co-publication patterns in EU. Scientometrics, 75, 555–574.CrossRefGoogle Scholar
  36. Mcpherson, J. M. (1982). Hypernetwork sampling: duality and differentiation among voluntary organizations ☆. Social Networks, 3, 225–249.CrossRefGoogle Scholar
  37. Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36, 363–377.CrossRefGoogle Scholar
  38. Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69, 213–238.CrossRefGoogle Scholar
  39. Newman, M. E. J. (2001). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64, 016131.CrossRefGoogle Scholar
  40. Newman, M. E. (2003a). Mixing patterns in networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 67, 026126.MathSciNetCrossRefGoogle Scholar
  41. Newman, M. E. J. (2003b). The structure and function of complex networks. SIAM Review, 45, 167–256.MathSciNetCrossRefzbMATHGoogle Scholar
  42. Pike, T. W. (2010). Collaboration networks and scientific impact among behavioral ecologists. Behavioral Ecology, 21, 431–435.CrossRefGoogle Scholar
  43. Porter, A. L., & Youtie, J. (2009). How interdisciplinary is nanotechnology? Journal of Nanoparticle Research, 11, 1023–1041.CrossRefGoogle Scholar
  44. Raan, A. F. J. V. (2006). Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups. Scientometrics, 67, 491–502.CrossRefGoogle Scholar
  45. Said, Y. H., Wegman, E. J., Sharabati, W. K., & Rigsby, J. T. (2008). RETRACTED: Social networks of author–coauthor relationships. Computational Statistics & Data Analysis, 52, 2177–2184.MathSciNetCrossRefGoogle Scholar
  46. Shahadat, U., Liaquat, H., & Kim, R. (2013). Network effects on scientific collaborations. PLoS ONE, 8, e57546.CrossRefGoogle Scholar
  47. Sigelman, L. (2009). Are two (or three or four…. or nine) heads better than one? Collaboration, multidisciplinarity, and publishability. PS Political Science and Politics, 42, 507–512.CrossRefGoogle Scholar
  48. Sonnenwald, D. H. (2014). Scientific collaboration. Annual Review of Information Science and Technology, 41, 643–681.CrossRefGoogle Scholar
  49. Taşkın, Z., & Aydinoglu, A. U. (2015). Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute. Scientometrics, 103, 1003–1022.CrossRefGoogle Scholar
  50. Thijs, B., & Glänzel, W. (2010). A structural analysis of collaboration between European research institutes. Research Evaluation, 19, 55–65.CrossRefGoogle Scholar
  51. 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, 14–26.CrossRefGoogle Scholar
  52. Wagner, C. S., Whetsell, T. A., & Leydesdorff, L. (2017). Growth of international collaboration in science: revisiting six specialties. Scientometrics, 110, 1633–1652.CrossRefGoogle Scholar
  53. Wallace, M. L., Larivière, V., & Gingras, Y. (2011). A small world of citations? The influence of collaboration networks on citation practices. PLoS ONE, 7, e33339.CrossRefGoogle Scholar
  54. White, J. C. (1992). Publication rates and trends in international collaborations for astronomers in developing countries, Eastern European countries, and the former Soviet Union. Publications of the Astronomical Society of the Pacific, 104, 472.CrossRefGoogle Scholar
  55. Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316, 1036–1039.CrossRefGoogle Scholar
  56. 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. Hoboken: Wiley.Google Scholar
  57. Zhou, J., Zeng, A., Fan, Y., & Di, Z. (2018). Identifying important scholars via directed scientific collaboration networks. Scientometrics, 114, 1327–1343.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementChina University of GeosciencesBeijingChina
  2. 2.Key Laboratory of Carrying Capacity Assessment for Resource and EnvironmentMinistry of Land and ResourcesBeijingChina
  3. 3.Key Laboratory of Strategic StudiesMinistry of Land and ResourcesBeijingChina

Personalised recommendations

Cite article

Buy options