Finding K-Most Influential Users in Social Networks for Information Diffusion Based on Network Structure and Different User Behavioral Patterns

Researches in the field of information propagation maximization consider that each content could have only one topic context and do not consider different propagation behaviors of people in different topics. In this paper, a new linear threshold diffusion model is presented which simulates the information diffusion among people with considering their different propagation behavior. Also a method is proposed for implementing the classic (basic) greedy algorithm to find the k-most influential users. The improved algorithm is called CBIG (content based improved greedy) algorithm which decreases the total amount of computations. Experimental results showed that the number of iterations of the algorithm were considerably decreased, while the precision of selecting most influential nodes is similar the base greedy algorithm.