Collaboration Networks Analysis: Combining Structural and Keyword-Based Approaches

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10546)


This paper proposes a method for the analysis of the characteristics of collaboration networks. The method uses social network analysis metrics which are especially applicable to directed and weighted collaboration networks. By using the proposed method it is possible to investigate the global structure of the collaboration networks, such as density, centralisation, assortativity and the dynamics of network growth. Furthermore, the method proposes appropriate network centrality measures (degree and its variations for directed and weighted networks) for ranking the nodes. In addition the proposed method combines a keyword-based approach and Louvain algorithm for the community detection task. Next, the paper describes a case study in which the proposed method is applied to the collaboration networks emerged from STSMs on the KEYSTONE COST Action.


Social networks analysis Collaboration networks Keyword-based community detection 


  1. 1.
    Abbasi, A., Hossain, L., Uddin, S., Rasmussen, K.J.: Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis. Scientometrics 89(2), 687–710 (2011)CrossRefGoogle Scholar
  2. 2.
    Abbasi, A., Altmann, J., Hossain, L.: 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. J. Informetrics 5(4), 594–607 (2011)CrossRefGoogle Scholar
  3. 3.
    Balland, P.A.: Proximity and the evolution of collaboration networks: evidence from research and development projects within the global navigation satellite system (GNSS) industry. Reg. Stud. 46(6), 741–756 (2012)CrossRefGoogle Scholar
  4. 4.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. ICWSM 8, 361–362 (2009)Google Scholar
  5. 5.
  6. 6.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  7. 7.
    Guan, J., Yan, Y., Zhang, J.J.: The impact of collaboration and knowledge networks on citations. J. Informetrics 11(2), 407–422 (2017)CrossRefGoogle Scholar
  8. 8.
    Guan, J., Zhang, J., Yan, Y.: The impact of multilevel networks on innovation. Res. Policy 44(3), 545–559 (2015)CrossRefGoogle Scholar
  9. 9.
    Hou, H., Kretschmer, H., Liu, Z.: The structure of scientific collaboration networks in Scientometrics. Scientometrics 75(2), 189–202 (2007)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    De Meo, P., Ferrara, E., Fiumara, G., Provetti, A.: Generalized Louvain method for community detection in large networks. In: 11th International Conference on Intelligent Systems Design and Applications, pp. 88–93. IEEE (2011)Google Scholar
  12. 12.
    Margan, D., Meštrović, A.: LaNCoA: a Python toolkit for language networks construction and analysis. In: MIPRO 2015, pp. 1628–1633 (2015)Google Scholar
  13. 13.
    Martinčić-Ipšić, S., Margan, D., Meštrović, A.: Multilayer network of language: a unified framework for structural analysis of linguistic subsystems. Phys. A Stat. Mech. Appl. 457, 117–128 (2016)CrossRefGoogle Scholar
  14. 14.
    Meštrović, A., Grubiša, Z.: Preliminary analysis of co-authorship networks at The University of Rijeka. Zbornik Veleucilista u Rijeci 3(1), 159–178 (2015)Google Scholar
  15. 15.
    Meštrović, A.: Semantic matching using concept lattice. In: Proceedings of Concept Discovery in Unstructured Data, Katholieke Universiteit Leuven, pp. 49–58 (2012)Google Scholar
  16. 16.
    Meštrović, A., Calì, A.: An ontology-based approach to information retrieval. In: Calì, A., Gorgan, D., Ugarte, M. (eds.) KEYSTONE 2016. LNCS, vol. 10151, pp. 150–156. Springer, Cham (2017). CrossRefGoogle Scholar
  17. 17.
    Newman, M.E.: The structure of scientific collaboration networks. Proc. Nat. Acad. Sci. 98(2), 404–409 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Newman, M.: Networks: An Introduction. Oxford University Press, Oxford (2010)CrossRefzbMATHGoogle Scholar
  19. 19.
    Roediger-Schluga, T., Barber, M.J.: R&D collaboration networks in the European Framework Programmes: Data processing, network construction and selected results. Int. J. Foresight Innov. Policy 4(3–4), 321–347 (2008)CrossRefGoogle Scholar
  20. 20.
    Savic, M., Ivanovic, M., Putnik, Z., Tütüncü, K., Budimac, Z., Smrikarova, S., Smrikarov, A.: Analysis of ERASMUS staff and student mobility network within a big European project. In: IEEE Mipro (2017)Google Scholar
  21. 21.
    Schilling, M.A., Phelps, C.C.: Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Manag. Sci. 53(7), 1113–1126 (2007)CrossRefzbMATHGoogle Scholar
  22. 22.
    Schult, D.A., Swart, P.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conferences (SciPy 2008), pp. 11–16 (2008)Google Scholar
  23. 23.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of RijekaRijekaCroatia

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