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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Domingos, P.: Mining social networks for viral marketing. IEEE Intelligent Systems 20, 80–82 (2005)CrossRefGoogle Scholar
  2. 2.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)Google Scholar
  3. 3.
    Goldenberg, K.J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 211–223 (2001)CrossRefGoogle Scholar
  4. 4.
    Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th International World Wide Web Conference, pp. 491–501 (2004)Google Scholar
  5. 5.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)Google Scholar
  6. 6.
    Kempe, D., Kleinberg, J., Tardos, E.: Influential nodes in a diffusion model for social networks. In: Proceedings of the 32nd International Colloquium on Automata, Languages and Programming, pp. 1127–1138 (2005)Google Scholar
  7. 7.
    Kimura, M., Saito, K.: Tractable models for information diffusion in social networks (Submitted for Publication, 2006)Google Scholar
  8. 8.
    McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 786–791 (2005)Google Scholar
  9. 9.
    Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization. Wiley, New York (1988)MATHGoogle Scholar
  10. 10.
    Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E 64, 016132 (2001)CrossRefGoogle Scholar
  11. 11.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  12. 12.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)Google Scholar

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

Personalised recommendations