A New Centrality Measure for Influence Maximization in Social Networks

  • Suman Kundu
  • C. A. Murthy
  • S. K. Pal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose a centrality measure for independent cascade model, which is based on diffusion probability (or propagation probability) and degree centrality. We use (i) centrality based heuristics with the proposed centrality measure to get k influential individuals. We have also found the same using (ii) high degree heuristics and (iii) degree discount heuristics. A Monte-Carlo simulation has been conducted with top k-nodes found through different methods. The result of simulation indicates, k nodes obtained through (i) significantly outperform those obtain by (ii) and (iii). We further verify the differences statistically using T-Test and found the minimum significance level (p-value) when k > 5 is 0.022 compare with (ii) and 0.015 when comparing with (iii) for twitter data.


Social Network Maximization Problem Centrality Measure Heuristic Model High Degree Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 199–208. ACM Press, New York (2009)CrossRefGoogle Scholar
  2. 2.
    Choudhury, M.D., Sundaram, H., John, A., Seligmann, D.D., Kelliher, A.: “birds of a feather”: Does user homophily impact information diffusion in social media? CoRR abs/1006.1702 (2010)Google Scholar
  3. 3.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 57–66. ACM Press, New York (2001)CrossRefGoogle Scholar
  4. 4.
    Estevez, P.a., Vera, P., Saito, K.: Selecting the Most Influential Nodes in Social Networks. In: International Joint Conference on Neural Networks, August 2007, pp. 2397–2402 (2007)Google Scholar
  5. 5.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 2003, p. 137. ACM Press, New York (2003)Google Scholar
  6. 6.
    López-Pintado, D.: Diffusion in complex social networks. Games and Economic Behavior 62(2), 573–590 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    MacKay, D.: Introduction to monte carlo methods. Learning in graphical models (1), 175–204 (1998)Google Scholar
  8. 8.
    Montgomery, D., Runger, G.: Applied Statistics And Probability For Engineers. Wiley, India (2007)zbMATHGoogle Scholar
  9. 9.
    Nieminen, J.: On the Centrality in a Graph. Scandinavian Journal of Psychology 15, 332–336 (1974)CrossRefGoogle Scholar
  10. 10.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 2002, p. 61 (2002)Google Scholar
  11. 11.
    Tang, J., Yao, L., Zhang, D., Zhang, J.: A combination approach to web user profiling. ACM Transactions on Knowledge Discovery from Data V(March), 1–38 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Suman Kundu
    • 1
  • C. A. Murthy
    • 1
  • S. K. Pal
    • 1
  1. 1.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

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