Optimal selection of seed nodes by reducing the influence of common nodes in the influence maximization problem

The Internet and social networks play an important role in today's world. Many large companies and organizations use the Internet to manage and implement their activities. Moreover, businesses in these organizations have been significantly expanded through social networks and the appropriate conditions provided by these networks for extensive advertising among a large number of users. Influence maximization (IM) can further spread news and advertisements in social networks by the optimal discovery of a set of k-member of users who have the most diffusion in the social network. Due to its importance, many researchers have investigated this problem and presented algorithms that cannot be used in the real world because they are not optimal and lack proper efficiency. Therefore, this paper reviews and studies the influence maximization problem (IMP) to provide an optimal solution to present an algorithm. The optimal solution is provided by the IBN algorithm at a suitable time. This algorithm prevents the diffusion to be limited among some nodes with common neighbors in a dense part of the network. This method solves the algorithm and selects a set of K members of influential nodes in two steps by scoring for each node and adjusting the scores according to the number of common neighbors between the nodes. The results obtained from this algorithm show that the proposed idea has an optimal solution for this problem, including very high accuracy in selecting the optimal nodes and suitable execution time compared to other algorithms.