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Scalable Influence Maximization in Social Networks Using the Community Discovery Algorithm

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2 Author(s)
Jinshuang Li ; Comput. Center, Northeastern Univ., Shenyang, China ; Yangyang Yu

Influence maximization is the problem of finding a small set of most influential vertices in a social network so that their aggregated influence in the network is maximized. Most social networks influence maximization problem are based on the following two basic propagation model: Independent Cascade Model and Linear Threshold Model. They all believe that the impact of all the vertices in a community is the same. It is inconsistent with the actual observed. in social networks, the influence of the different members in a community is not the same. Every community have some core members, their influence is far greater than the others. in view of this, a community discovery algorithm is proposed to find the core members of the community. Selecting the initial members from these core members will have the greatest influence.

Published in:

Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on

Date of Conference:

25-28 Aug. 2012

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