A Dynamic Metaheuristic Algorithm for Influence Maximization in Social Networks

During the very last decade, people have been spending lots of time working with social networks to interact with friends and to share information, thoughts, news, and etc. These social networks comprise a very important part of our daily lives. Along with the exploitation of the development of social networks, finding influential individuals in a social network has many practical functions in marketing, politics, and even control of the diseases. In the present research, a novel method called the dynamic generalized vulture algorithm has been proposed to solve influence maximization problems. Regarding the fact that in real world social networks own very dynamic and scalable nature, through our proposed algorithm, we have considered two important criteria which have been rarely taken into consideration in previous projects. The first criterion is due to the network structure change during time pass and the other refers to scalability. The suggested algorithm was measured considering standard data sets. The results showed that the proposed algorithm has been more scalable and has had higher precision in locating the most influential tops in such networks compared with other algorithms due to the reduction of search area and using several different mechanisms during navigation and optimization, balance creation and moving through these stages.