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A New Centrality Measure for Influence Maximization in Social Networks

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

Abstract

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.

Keywords

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.

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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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