, Volume 109, Issue 3, pp 1561–1578 | Cite as

Core-periphery dynamics in collaboration networks: the case study of Slovenia

  • Mario KarlovčecEmail author
  • Borut Lužar
  • Dunja Mladenić


The paper presents analysis of core-periphery structure and transition dynamics of individuals between the core and periphery in collaboration networks of Slovenian researchers over 44 years. We observe the dynamics of individuals from three different aspects: regarding the length of the presence in the core-strength, the length of intervals of permanent presence in the core-stability, and the presence in different time periods-balance. We use clustering and classification machine learning techniques in order to automatically group individuals with similar dynamics of behaviour into common classes. We study collaboration networks of Slovenian researchers based on their publication records. The data we used comprises about 18,000 researchers registered in Slovenian national databases of researchers together with their publications from 1970 to 2013.


Core-periphery structure Collaboration network Machine learning Clustering 



The authors thank two anonymous referees for their remarks which helped to improve the manuscript. This research was initiated in the framework of the project L7–4119 financed by Slovenian Research Agency ARRS. The second author acknowledges a partial support from ARRS Program P1–0383, Creative Core-FISNM–3330–13–500033, and the National Scholarship Programme of the Slovak Republic.


  1. Alba, R. D., & Moore, G. (1978). Elite social circles. Sociological Methods and Research, 7(2), 167–188.CrossRefGoogle Scholar
  2. Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Borgatti, S., & Everett, M. (1999). Models of core/periphery structures. Social Networks, 21, 375–395.CrossRefGoogle Scholar
  4. Borgatti, S., Everett, M. G., & Freeman, L. C. (2002). UCINET 6 for Windows: Software for social network analysis. Harvard: Analytic Technologies.Google Scholar
  5. Boyd, J. P., Fitzgerald, W. J., & Beck, R. J. (2006). Computing core/periphery structures and permutation tests for social relations data. Social Networks, 28(2), 165–178.CrossRefGoogle Scholar
  6. Brusco, M. (2011). An exact algorithm for a core/periphery bipartitioning problem. Social Networks, 33(1), 12–19.MathSciNetCrossRefGoogle Scholar
  7. Doreian, P., & Fararo, T. J. (1985). Structural equivalence in a journal network. Journal of the American Society for Information Science and Technology, 36, 28–37.CrossRefGoogle Scholar
  8. Glänzel, W. (2001a). Coauthorship patterns and trends in the sciences (1980–1998): A bibliometric study with implications for database indexing and search strategies. Library Trends, 50(3), 461–473.Google Scholar
  9. Glänzel, W. (2001b). National characteristics in international scientific co-authorship relations. Scientometrics, 51(1), 69–115.MathSciNetCrossRefGoogle Scholar
  10. Glänzel, W., & Schubert, A. (2005). The use of publication and patent statistics in studies of S&T systems. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  11. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning (1st ed.). Boston: Addison-Wesley Longman Publishing Co., Inc.zbMATHGoogle Scholar
  12. Hâncean, M. G., Perc, M., & Vlăsceanu, L. (2014). Fragmented romanian sociology: Growth and structure of the collaboration network. PLoS One, 9(11), e113271.CrossRefGoogle Scholar
  13. Harenberg, S., Bello, G., Gjeltema, L., Ranshous, S., Harlalka, J., Seay, R., et al. (2014). Community detection in large-scale networks: A survey and empirical evaluation. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 426–439.CrossRefGoogle Scholar
  14. Ioannidis, J. P. A., Boyack, K. W., & Klavans, R. (2014). Estimates of the continuously publishing core in the scientific workforce. PLoS One, 9(7), e101698.CrossRefGoogle Scholar
  15. Jovanović, M. M., John, M., & Reschke, S. (2010). Effects of civil war: Scientific cooperation in the republics of the former Yugoslavia and the province of Kosovo. Scientometrics, 82(3), 627–645.CrossRefGoogle Scholar
  16. 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
  17. Kernighan, B. W., & Lin, S. (1970). An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal, 49(2), 291–307.CrossRefzbMATHGoogle Scholar
  18. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kronegger, L., Mali, F., Ferligoj, A., & Doreian, P. (2012). Collaboration structures in Slovenian scientific communities. Scientometrics, 90(2), 631–647.CrossRefGoogle Scholar
  20. Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data, 1(1), 2.CrossRefGoogle Scholar
  21. Leskovec, J., Backstrom, L., Kumar, R., & Tomkins, A. (2008). Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD ’08, (pp. 462–470).Google Scholar
  22. Lip, S. Z. W. (2011). A fast algorithm for the discrete core/periphery bipartitioning problem. ArXiv preprint, article no. 1102.5511.Google Scholar
  23. Lužar, B., Levnajić, Z., Povh, J., & Perc, M. (2014). Community structure and the evolution of interdisciplinarity in slovenia’s scientific collaboration network. PLoS One, 9(4), e94429.CrossRefGoogle Scholar
  24. Mali, F., Kronegger, L., & Ferligoj, A. (2010). Co-authorship trends and collaboration patterns in the Slovenian sociological community. Corvinus Journal of Sociology and Social Policy, 2(1), 29–50.Google Scholar
  25. Mcauley, J., & Leskovec, J. (2014). Discovering social circles in ego networks. ACM Transactions on Knowledge Discovery from Data, 8(1), 4.CrossRefGoogle Scholar
  26. Nemeth, R. J., & Smith, D. A. (1985). International trade and world-system structure: A multiple network analysis. Review, 8(4), 517–560.Google Scholar
  27. Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.MathSciNetCrossRefzbMATHGoogle Scholar
  28. Perc, M. (2010a). Growth and structure of Slovenia’s scientific collaboration network. Journal of Informetrics, 4(4), 475–482.CrossRefGoogle Scholar
  29. Perc, M. (2010b). Zipf’s law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia’s research as an example. Journal of Informetrics, 4(3), 358–364.CrossRefGoogle Scholar
  30. Perc, M. (2014). The matthew effect in empirical data. Journal of the Royal Society Interface, 11(98), 20140378.CrossRefGoogle Scholar
  31. Smith, D. A., & White, D. R. (1992). Structure and dynamics of the global economy: Network analysis of international trade 1965–1980. Social Forces, 70(4), 857–893.CrossRefGoogle Scholar
  32. Snyder, D., & Kick, E. L. (1979). Structural position in the world system and economic growth, 1955–1970: A multiple-network analysis of transnational interactions. American Journal of Sociology, 84(5), 1096–1126.CrossRefGoogle Scholar
  33. Wallerstein, I. M. (1974). The modern world-system I: Capitalist agriculture and the origins of the European world-economy in the sixteenth century (Studies in Social Discontinuity). New York: Academic Press.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.Faculty of Information StudiesNovo MestoSlovenia

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