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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%. [ABSTRACT FROM AUTHOR]

Influence maximization problem aims to find a set of nodes with the highest diffusion in social networks in order to maximize diffusion in the graph by this set. A set of these nodes can be used to diffuse news, viruses , marketing and etc. To solve this problem, some algorithms have been proposed to help identify a set of nodes. Due to the low accuracy and high run time in selecting the set of nodes by the proposed algorithms, further studies should be performed in this area. This paper intends to solve the Richclub problem in selecting nodes, reduce the search space to decrease computational overhead, and achieve increases the algorithm accuracy by selecting high-charisma nodes. The Charismatic Transmission in Influence Maximization (CTIM) algorithm reduces the computational overhead by using community structure and pruning criteria. In this algorithm, seed nodes are selected using nodes that have high charismatic power. Also, these nodes have a high correlation with influential nodes in other communities, which causes optimal diffusion in social networks. As a result a set of seed nodes are selected by calculating the influence spread, which increases the algorithm accuracy. In all size ranges of seed sets and different data sets, experiment results show The CTIM algorithm performs well in influence spread. Also in The douban dataset significantly outperforms the PHG algorithm to as much as a 6.56% increase in influence spread and the execution time has decreased by 99%. [ABSTRACT FROM AUTHOR]