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Abstract: The problem of spreading information is a topic of considerable recent interest, but the traditional influence maximization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbitrary k nodes in a network, we have developed a decentralized version of the influential maximization problem by influencing k neighbours rather than arbitrary users in the entire network. We present several reasonable neighbour selection schemes and evaluate their performance with a real dataset collected from Twitter. Unlike previous studies using net- work topology alone or synthetic parameters, we use real propagation rate for each node calculated from the Twitter messages during the 2010 UK election campaign. Our experimental results show that information can be efficiently propagated in online social networks using neighbours with a high propagation rate rather than those with a high number of neighbours.
Keyword(s): Information diffusion ; Information dissemination ; Online social networks ; Viral marketing
Published in: Finding Influential Neighbors to Maximize Information Diffusion in Twitter, Hyoungshick Kim, Konstantin Beznosov, and Eiko Yoneki, WWW’14 Companion, April 7–11, 2014, Seoul, Korea.:
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Refereed Conference Papers