Advertisement

A Network Formation Model for Collaboration Networks

  • Ankur Sharma
  • S. Durga Bhavani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)

Abstract

In social networks, a network grows by following certain rules and patterns, e.g. a collaboration network in which authors come together and publish an article. These authors might have collaborated previously, or they may collaborate in the future with other authors. That is how a collaboration network grows. Collaboration networks are represented as graphs where nodes denote authors and edges between nodes indicate a collaboration between the corresponding authors. There are very few network formation models specific to collaboration networks in the literature. In this work, a novel network formation model that can imitate the growth of a collaboration network is proposed. The main idea is based on the arrival distribution of the numbers of authors collaborating for the papers. We find that Exponential distribution matches best for this process simulation. We have used DBLP dataset to analyze and find the patterns in the network. We show that the network generated by the proposed model is closer to the original network than that of Shi et al. The model has to be further refined in order to improve the results for average clustering coefficient and density of the network.

Keywords

Network formation model Social networks Collaboration network DBLP 

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    DBLP: Computer science bibliography. https://dblp.uni-trier.de
  5. 5.
    Barabasi, A.L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A: Statist. Mech. Appl. 311, 590–614 (2002).  https://doi.org/10.1016/S0378-4371(02)00736-7MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006).  https://doi.org/10.1007/978-1-4615-7566-5CrossRefzbMATHGoogle Scholar
  8. 8.
    Schreiber, F., Junker, B.H.: Analysis of Biological Networks. Wiley, Hoboken (2007)Google Scholar
  9. 9.
    Kullback, S., Leibler, R.: On information and sufficiency. Ann. Math. Statist. 22(1), 79–86 (1951)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lakshmi, T.J., Bhavani, S.D.: Temporal probabilistic measure for link prediction in collaborative networks. Appl. Intell. 47(1), 83–95 (2017)CrossRefGoogle Scholar
  11. 11.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: 11th International Conference on Knowledge Discovery and Data mining, pp. 177–187 (2005)Google Scholar
  12. 12.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  13. 13.
    Middendorf, M., Ziv, E., Wiggins, C.H.: Inferring network mechanisms: the Drosophila melanogaster protein interaction network. Proc. Natl. Acad. Sci. 102, 3192–3197 (2005)CrossRefGoogle Scholar
  14. 14.
    Milo, R., Kashtan, N., Itzkovitz, S., Newman, M.E.J., Alon, U.: On the uniform generation of random graphs with prescribed degree sequences. arXiv e-prints (2003)Google Scholar
  15. 15.
    Newman, M.E.J.: Scientific collaboration networks. i. Network construction and fundamental results. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. (2001).  https://doi.org/10.1103/PhysRevE.64.016131
  16. 16.
    Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98, 404–409 (2001)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Newman, M.E.J.: Networks: An Introduction. Oxford University Press, Oxford (2010)CrossRefGoogle Scholar
  18. 18.
    Navlakha, S., Kingsford, C.: Network archaeology: uncovering ancient networks from present-day interactions. PLoS Comput. Biol. 7(4), e1001119 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Shi, X., Wu, L., Yang, H.: Scientific collaboration network evolution model based on motif emerging. In: The 9th International Conference for Young Computer Scientists, pp. 2748–2752 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer and Information SciencesUniversity of HyderabadHyderabadIndia

Personalised recommendations