LKG: A fast scalable community-based approach for influence maximization problem in social networks

Abstract

The detection of top influential users in social networks is considered one of the current vital research field. The spreading of the information in social networks can be analyzed and sometimes controlled by studying those top influential users. This paper proposes LKG, a fast and scalable hybrid approach to detect top influential users in social networks, suitable for both directed and undirected networks. The LKG hybrid approach consists of three phases: (1) community detection, in which the complete social network is partitioned into related communities using the Louvain algorithm; (2) detection of community top nodes by applying the k-shell decomposition locally in each portioned community; and (3) selection generalization, in which the prior obtained results are generalized for maximizing the spread of influence. Experimental studies were conducted on several datasets with different sizes. The results have been shown to achieve better results for the spread of influence using incomplete social networks than the existing related work models and with far much less processing time.