Determination of influential nodes based on the Communities’ structure to maximize influence in social networks

Abstract

With the increasing development of social networks, they have turned into important research platforms. Influence maximization is one of the most important research issues in the field of social networks. This problem detects influential k-node with the greatest influence spread. The influence maximization faces two important challenges, time efficiency and optimal selection of seed nodes. In order to solve such challenges, we propose an algorithm based on optimal pruning and scoring adjustment, which is called IMBC for short. The IMBC (Influence Maximization Based on Community structure) algorithm uses optimal pruning and a minimum of dominating nodes to improve time efficiency. In addition, for optimal selection of seed nodes, the IMBC algorithm modulates the scores of nodes with a high Rich-Club coefficient. In order to select influential nodes, we first select an optimal set using the minimum dominating nodes and node scores, with the aim of optimal pruning in influence spread calculations. Because large-scale social networks have many nodes, optimal pruning reduces computational overhead. Then, the seed nodes are selected based on the scoring adjustment. Scoring adjustment is done to avoid the Rich Club phenomenon because avoiding this phenomenon causes a large amount of diffusion in social networks. The experimental results show that the proposed algorithm performs better than the algorithms presented in recent years in influence spread and runtime. Therefore, the IMBC algorithm is a balance between quality and efficiency. Also, in the PGP dataset results, the PHG algorithm with as much as a 5.08% increase in influence spread, and the runtime has decreased by 97%.