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Approximate Solutions for the Influence Maximization Problem in a Social Network

  • Masahiro Kimura
  • Kazumi Saito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

We address the problem of maximizing the spread of information in a large-scale social network based on the Independent Cascade Model (ICM). When we solve the influence maximization problem, that is, the optimization problem of selecting the most influential nodes, we need to compute the expected number of nodes influenced by a given set of nodes. However, an exact calculation or a good estimate of this quantity needs a large amount of computation. Thus, very large computational quantities are needed to approximately solve the influence maximization problem based on a natural greedy algorithm. In this paper, we propose methods to efficiently obtain good approximate solutions for the influence maximization problem in the case where the propagation probabilities through links are small. Using real data on a large-scale blog network, we experimentally demonstrate the effectiveness of the proposed methods.

Keywords

Social Network Greedy Algorithm Maximization Problem Submodular Function Approximation Guarantee 
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 2006

Authors and Affiliations

  • Masahiro Kimura
    • 1
  • Kazumi Saito
    • 2
  1. 1.Department of Electronics and InformaticsRyukoku UniversityOtsuJapan
  2. 2.NTT Communication Science LaboratoriesNTT CorporationSeika-cho, KyotoJapan

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