A Comparative Analysis of Different Social Network Parameters Derived from Facebook Profiles

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

Abstract

In social network analysis (SNA), using online social media, it is possible to collect large open source information and to analyze those data for knowing the characteristics of these networks. The main objective of this work is to study online social network parameters commonly used to explain social structures. In this paper, we have extracted data from the three real-time facebook accounts using Netvizz application. Gephi, a open source free software, is used for analysis and evaluation of these network parameters. This analysis shows some well-known network parameters like calculating clustering coefficient (CC) of clusters, group formation, finding node degree distribution (NDD), identifying influential node etc., which can be used for further feature extraction.

Keywords

Social network analysis Facebook profiles Social network parameters Netvizz Gephi 

Notes

Acknowledgments

We would like to thank Subrata Dey, Aniket Sinha and Dr. Krishnendu Dutta for sharing their Facebook profiles with us for data collection purposes.

References

  1. 1.
    Benevenuto, F., Rodrigues, F., Cha, M., Almeida.: Characterizing user behavior in online social networks. In: Proceedings of ACM IMC (2009)Google Scholar
  2. 2.
    Rieder, B.: Studying facebook via data extraction: the Netvizz application. In: WebSci, Paris, France, ACM 978-1-4503-1889-1, May 24 2013Google Scholar
  3. 3.
    Wilson, R.E., Gosling, S.D., Graham, L.T.: A review of facebook research in the social sciences perspectives. Psychol. Sci. 7(3), 203–220 (2012)Google Scholar
  4. 4.
    Gephi Official Website: https://gephi.github.io
  5. 5.
    Jacomy, M., Heymann, S., Venturini, T., Bastian, M.: ForceAtlas2, a continuous graph layout algorithm for handy network visualization. PloS One 9(6), (2010)Google Scholar
  6. 6.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw: Pract. Expert 21(11), 1129–1164 (1991)Google Scholar
  7. 7.
    Wayne, G., Oellermann, R.O.: Distance in Graphs, Structural Analysis of Complex Networks, pp. 49–72. Springer (2011)Google Scholar
  8. 8.
    Wang, T., Chen, Y., Zhang, Xu, T., Jin, L., Hui, P., Deng, B., Li, X.: Understanding graph sampling algorithms for social network analysis. In: Simplex, IEEE ICDCS, Minneapolis, USA, pp. 123–128 June 20–24 2011Google Scholar
  9. 9.
    Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proceedings of 9th ACM-SIAM Symposium on Discrete Algorithms (1998)Google Scholar
  10. 10.
    Freeman, L.C.: Centrality in social networks: conceptual clarification. Soc. Netw. 1, 215–239 (1978/1979)Google Scholar
  11. 11.
    Ronald, S.B.: Social contagion and innovation: Cohesion versus structural equivalence. Am. J. Sociol. 92(6), 1287–1335 (1987)CrossRefGoogle Scholar
  12. 12.
    Fortunato, S., Castellano, C.: Community structure in graphs. Phys. Rep. (2007)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Information TechnologyGCECTKolkataIndia
  2. 2.Department of Computer Science & EngineeringJadavpur UniversityKolkataIndia

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