In other words, the goal of the influence maximization problem is to find initial active people who have the most influence on other people in a short time under a diffusion model. The influence maximization problem under both IC and LT models are NP-hard. The influence of seed nodes is determined by the number of nodes activated. The diffusion process of the seed nodes is performed to maximize the influence spread. Algorithms in this problem take the graph G and a number k as input and generate seed set, with the intention that the expected number of nodes influenced by the seed set by the stochastic process of the diffusion model, One major problem in the influence maximization problem to maximize the expected size of the final active set, given some constraints on the seed set. The influence maximization problem identifies the active nodes as seed nodes. To solve this challenge, the influence maximization problem in social networks has been provided. Given the limited advertising resources, the main challenge in this problem is to select a specific set of influential persons that most effectively on other people in a short time. That’s why business companies are using the speed of information diffusion on social networks to achieve widespread advertising and business optimization. Consequently, information diffusion occurs across a wide range of social networks at high speed. The social networks like LinkedIn, Facebook, and Twitter provide the grounds for people to interact with no time and space limitation.
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