Scalable Influence Maximization in Social Networks Using the Community Discovery Algorithm

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.