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Abstract During the last decade, social networks have received particular attention as a new platform for advertising and the diffusion of information, especially in viral marketing. In addition, we recently witnessed that the structure of social networks is continuously changing and expanding in terms of size and complexity. Generally, social networks consist of nodes and communication edges between them. Precisely, these edges determine the communication between people in the network. This communication can be supportive, educational, friendly, political, etc., depending on the type of social network. When we consider the fact that humans with all their unique qualities, mentally and physically, are the nodes of these social networks, we face challenges and complications that cannot be solved and paid attention in any of the previous methods for the IM problem in the past. In this paper, we employed an optimized heuristic to disseminate practical information in the least amount of time, followed by leveraging social reinforcement and memory. Furthermore, by utilizing an advanced and efficient genetic algorithm with novel and enhanced operators, we can effectively address the Influence Maximization problem and identify the most influential set of seeds within the dynamic social network, as demonstrated in the experiments section.
Iranmanesh et al. (Thu,) studied this question.