独立级联模型中基于社区的影响最大化( Community based influence maximization in the Independent Cascade Model )

L Hajdu A Bóta M Krész

—Community detection is a widely discussed topicin network science which allows us to discover detailed in-formation about the connections between members of a givengroup. Communities play a critical role in the spreading ofviruses or the diffusion of information. In [1], [8] Kempe et al.proposed the Independent Cascade Model, defining a simple setof rules that describe how information spreads in an arbitrarynetwork. In the same paper the influence maximization problemis defined. In this problem we are looking for the initial vertex setwhich maximizes the expected number of the infected vertices.The main objective of this paper is to further improve theefficiency of influence maximization by incorporating informationon the community structure of the network into the optimizationprocess. We present different community-based improvementsfor the infection maximization problem, and compare the resultsby running the greedy maximization method.

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