Problema de minimização de custo de propagação de influência em redes sociais: uma abordagem meta-heurística

Abstract

With the growing use of social media, companies have increasingly sought to link their products to digital influencers, as this has proven to be an effective way to promote products and consequently increase sales. However, the decision of which influencer to hire with the best cost-benefit ratio can become a challenging task, especially when considering the scalability of networks. Aiming to present solutions to this problem, this work aimed to study and propose improvements to one of the metaheuristic algorithms found in the literature, for solving the Influence Propagation Cost Minimization Problem in Social Networks. This problem seeks to find a fixed number of influencers in a social network who can disseminate advertisements for a product through their profiles. After conducting tests and statistical analyses using the same instances presented by the author of the studied metaheuristic algorithm, we concluded that the proposed improvements indeed result in performance gains, improving the average GAP by 34,47%.