Adaptive Evolutionary Algorithm for Maximizing Social Influence

Influence Maximization (IM) is an issue that is represented by a predetermined group of users, sometimes referred to as the seed. The latter can have an impact on their friends, who can then have an impact on other people, and so on, until the network has the most users affected. According to it, the seeds should be properly chosen to ensure widespread information dissemination. The motivation for beginning this effort by building two models is the rate of user interactions on one side and the density of relationships on another side using the classic Independent Cascade (IC) as a diffusion model. IM has been modeled in both models as a genetic algorithm optimization issue. In the first model, the population with a high rate of interaction is mostly used to represent the population. In contrast, in the second, the population is represented by people who have a high relationship density. The Higgs, Digg, and Twitter Dynamic networks were used in an experimental framework to compare the two hypotheses with the standard model. According to the findings, the suggested technique can boost the impact spread from the baseline model by 6% to 200%.