Tractable Models for Information Diffusion in Social Networks

  • Masahiro Kimura
  • Kazumi Saito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


When we consider the problem of finding influential nodes for information diffusion in a large-scale social network based on the Independent Cascade Model (ICM), we need to compute the expected number of nodes influenced by a given set of nodes. However, a good estimate of this quantity needs a large amount of computation in the ICM. In this paper, we propose two natural special cases of the ICM such that a good estimate of this quantity can be efficiently computed. Using real large-scale social networks, we experimentally demonstrate that for extracting influential nodes, the proposed models can provide novel ranking methods that are different from the ICM, typical methods of social network analysis, and “PageRank” method. Moreover, we experimentally demonstrate that when the propagation probabilities through links are small, they can give good approximations to the ICM for finding sets of influential nodes.


Social Network Maximization Problem Social Network Analysis Betweenness Centrality Ranking Method 
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 UniversityOtsu, ShigaJapan
  2. 2.NTT Communication Science LaboratoriesNTT CorporationKyotoJapan

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