Parallel social behavior-based algorithm for identification of influential users in social network

Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user's interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.