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Important institutions of interinstitutional scientific collaboration networks in materials science

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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.

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References

  • 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 

  • Adams, J. (2012). Collaborations: The rise of research networks. Nature, 490, 335.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Avkiran, N. K. (2013). An empirical investigation of the influence of collaboration in Finance on article impact. Scientometrics, 95, 911–925.

    Article  Google Scholar 

  • Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  • 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.

    MathSciNet  MATH  Google Scholar 

  • Bhattacharyya, M., & Bandyopadhyay, S. (2015). Finding quasi core with simulated stacked neural networks. Information Sciences, 294, 1–14.

    Article  MathSciNet  MATH  Google Scholar 

  • Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23, 191–201.

    Article  Google Scholar 

  • Bornmann, L., & Daniel, H. D. (2005). Does the h-index for ranking of scientists really work? Scientometrics, 65, 391–392.

    Article  Google Scholar 

  • Breiger, R. L. (1974). The Duality of Persons and Groups. Social Forces, 53, 181–190.

    Article  Google Scholar 

  • Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and methods in social network analysis. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chang, H. W., & Huang, M. H. (2013). Prominent institutions in international collaboration network in astronomy and astrophysics. New York: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dorogovtsev, S. N., & Mendes, J. F. F. (2002). Evolution of networks. Advances in Physics, 51, 1079–1187.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ebadi, A., & Schiffauerova, A. (2015). How to become an important player in scientific collaboration networks? Journal of Informetrics, 9, 809–825.

    Article  Google Scholar 

  • Egghe, L., & Rousseau, R. (1990). Introduction to Informetrics. Information Processing and Management, 28, 1–3.

    Google Scholar 

  • Fafchamps, M., Leij, M. J. V. D., & Goyal, S. (2010). Matching and network effects. Journal of the European Economic Association, 8, 203–231.

    Article  Google Scholar 

  • Freeman, L. C., Roeder, D., & Mulholland, R. R. (1980). Centrality in social networks: II. experimental result. Social Networks, 2, 119–141.

    Article  Google Scholar 

  • Fuchs, C. (2017). Sustainability and community networks. Telematics and Informatics, 34, 628–639.

    Article  Google Scholar 

  • Gazni, A., & Thelwall, M. (2016). The citation impact of collaboration between top institutions: A temporal analysis. Research Evaluation, 25, 219–229.

    Article  Google Scholar 

  • Ghosh, J., Kshitij, A., & Kadyan, S. (2015). Functional information characteristics of large-scale research collaboration: network measures and implications. Scientometrics, 102, 1207–1239.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kockelmans, J. J. (Ed.). (1979). Interdisciplinarity and higher education. State College: The Pennysylvania State Univ.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Latora, V., Nicosia, V., & Panzarasa, P. (2013). Social cohesion, structural holes, and a tale of two measures. Journal of Statistical Physics, 151, 745–764.

    Article  MathSciNet  MATH  Google Scholar 

  • 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 

  • Li, J., & Li, Y. (2015). Patterns and evolution of coauthorship in China’s humanities and social sciences. Scientometrics, 102, 1997–2010.

    Article  Google Scholar 

  • Ma, A., & Mondragón, R. J. (2015). Rich-cores in networks. PLoS ONE, 10, e0119678.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mcpherson, J. M. (1982). Hypernetwork sampling: duality and differentiation among voluntary organizations ☆. Social Networks, 3, 225–249.

    Article  Google Scholar 

  • Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36, 363–377.

    Article  Google Scholar 

  • Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69, 213–238.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Newman, M. E. (2003a). Mixing patterns in networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 67, 026126.

    Article  MathSciNet  Google Scholar 

  • Newman, M. E. J. (2003b). The structure and function of complex networks. SIAM Review, 45, 167–256.

    Article  MathSciNet  MATH  Google Scholar 

  • Pike, T. W. (2010). Collaboration networks and scientific impact among behavioral ecologists. Behavioral Ecology, 21, 431–435.

    Article  Google Scholar 

  • Porter, A. L., & Youtie, J. (2009). How interdisciplinary is nanotechnology? Journal of Nanoparticle Research, 11, 1023–1041.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Shahadat, U., Liaquat, H., & Kim, R. (2013). Network effects on scientific collaborations. PLoS ONE, 8, e57546.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sonnenwald, D. H. (2014). Scientific collaboration. Annual Review of Information Science and Technology, 41, 643–681.

    Article  Google Scholar 

  • Taşkın, Z., & Aydinoglu, A. U. (2015). Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute. Scientometrics, 103, 1003–1022.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wagner, C. S., Whetsell, T. A., & Leydesdorff, L. (2017). Growth of international collaboration in science: revisiting six specialties. Scientometrics, 110, 1633–1652.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316, 1036–1039.

    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. Hoboken: Wiley.

    Google Scholar 

  • Zhou, J., Zeng, A., Fan, Y., & Di, Z. (2018). Identifying important scholars via directed scientific collaboration networks. Scientometrics, 114, 1327–1343.

    Article  Google Scholar 

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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.

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, Beijing, 100083, China

    Yang Li, Huajiao Li, Nairong Liu & Xueyong Liu

  2. Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources, Beijing, 100083, China

    Yang Li, Huajiao Li, Nairong Liu & Xueyong Liu

  3. Key Laboratory of Strategic Studies, Ministry of Land and Resources, Beijing, 100812, China

    Yang Li, Huajiao Li, Nairong Liu & Xueyong Liu

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  1. Yang Li
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Correspondence to Huajiao Li.

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Li, Y., Li, H., Liu, N. et al. Important institutions of interinstitutional scientific collaboration networks in materials science. Scientometrics 117, 85–103 (2018). https://doi.org/10.1007/s11192-018-2837-0

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