Published September 26, 2018 | Version v1
Conference paper Open

Community based influence maximization in the Independent Cascade Model

  • 1. University of Szeged Institute of Informatics
  • 2. Innorenew CoE, University of Primorska Andrej Marušic Institute, University of Szeged Gyula Juhász Faculty of Education,
  • 3. University of Szeged Gyula Juhász Faculty of Education


Community detection is a widely discussed topic in network science which allows us to discover detailed information about the connections between members of a given group. Communities play a critical role in the spreading of viruses or the diffusion of information. In [1], [8] Kempe et al. proposed the Independent Cascade Model, defining a simple set of rules that describe how information spreads in an arbitrary network. In the same paper the influence maximization problem is defined. In this problem we are looking for the initial vertex set which maximizes the expected number of the infected vertices. The main objective of this paper is to further improve the efficiency of influence maximization by incorporating information on the community structure of the network into the optimization process. We present different community-based improvements for the infection maximization problem, and compare the results by running the greedy maximization method.



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InnoRenew CoE – Renewable materials and healthy environments research and innovation centre of excellence 739574
European Commission