Spreader Selection by Community to Maximize Information Diffusion in Social Networks

Rumors or information can spread quickly and reach many users in social networks. Models for understanding, preventing or increasing the diffusion of information are of greatest interest to companies, governments, scientists, etc. In this paper, we propose an approach for maximizing the information diffusion by selecting the most important (central) users from communities. We also analyze the selection of the most central vertices of the network and considered artificial and real social networks, such as email, hamsterster, advogato and astrophysics. Experimental results confirmed the improvement of the final fraction of informed individuals by applying the proposed approach.