Advertisement

A Shapley Value Approach for Community Detection in Social Network

  • Amreen AhmadEmail author
  • Tanvir Ahmad
  • Abhishek Bhatt
  • Sadaf Siddiqui
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 847)

Abstract

The increasing popularity of online social networks (OSNs) has attracted significant attention from the research community. One of the significant tasks of analyzing structure is to detect communities within these OSN. Nodes within a community tend to be densely connected in comparison with nodes outside the community. Community detection is a challenging task in the field of social networks (SNs). Graph model is used to represent OSN where the nodes are representative of actors and the edges represent the interactions between these actors. A novel community detection algorithm named SCD is proposed in this paper. In the underlying graph, nodes are modeled as rational players where each player wants to maximize its Shapley value in terms of interactions. Extensive experiments are conducted on real-world datasets to establish the efficiency of the proposed approach.

Keywords

Online social network Community detection Shapley value 

References

  1. 1.
    Chen, J., Yuan, B.: Detecting functional modules in the yeast protein–protein interaction network. Bioinformatics 22(18), 2283 (2006)CrossRefGoogle Scholar
  2. 2.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Flake, G.W., Lawrence, S.R., Giles, C.L., Coetzee, F.M.: Self-organization and identification of web communities. IEEE Comput. 35, 66–71 (2002)CrossRefGoogle Scholar
  4. 4.
    Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM workshop on Online Social Networks, pp. 37–42. ACM, New York (2009)Google Scholar
  5. 5.
    Narayanam, R., Narahari, Y.: A shapley value-based approach to discover influential nodes in social networks. IEEE Trans. Autom. Sci. Eng., pp. 1–18 (2011)Google Scholar
  6. 6.
    Zachary, W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)CrossRefGoogle Scholar
  7. 7.
    Lusseau, D.: The emergent properties of a dolphin social network. Proc. Natl. Acad. Sci. 270(Suppl. no. 0962-8452), S186–8 (2003)Google Scholar
  8. 8.
    Saida, A., Abbasi, R.A., Maqbool, O., Daudb, A., Aljohani, N.R.: CC-GA: a clustering coefficient based genetic algorithm for detecting communities in social networks. Appl. Soft Comput., pp. 59–70 (2018)CrossRefGoogle Scholar
  9. 9.
    Moore, C., Newman, M.E.J.: Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 61, 5678–5682 (2000)CrossRefGoogle Scholar
  10. 10.
    Raju, E., Hameed, M.A., Sravanthi, K.: Detecting communities in social networks using unnormalized spectral clustering incorporated with bisecting K-means. In: Proceedings of 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Amreen Ahmad
    • 1
    Email author
  • Tanvir Ahmad
    • 1
  • Abhishek Bhatt
    • 1
  • Sadaf Siddiqui
    • 1
  1. 1.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia

Personalised recommendations