Abstract
Influence maximization (IM), aiming at selecting initial users as seeds to maximize the influence spread, has become a vital problem in social network applications such as viral marketing and friend recommendation. Existing IM diffusion models ignore the behaviors that users readily conform to the actions of others in the group, which leads to incomprehensive information propagation processes and biased spread results. In this paper, we propose a group-based influence maximization (GIM) algorithm to solve the IM problem over the conformity-aware diffusion model which utilizes different types of conformity behaviors based on user profiles and group profiling. Experimental results confirm the effectiveness and efficiency for our GIM algorithm against other baseline IM algorithms.
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