Riquelme, FabianMuñoz, FranciscoOlivares, Rodrigo2022-11-302022-11-302021Fabian Riquelme, Francisco Muñoz, and Rodrigo Olivares. 2022. Social influence under improved multi-objective metaheuristics. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '21). Association for Computing Machinery, New York, NY, USA, 479–486. https://doi.org/10.1145/3487351.3488558http://repositoriobibliotecas.uv.cl/handle/uvscl/7514The influence maximization problem (IMP) and the least cost influence problem (LCI) are two relevant and widely studied problems in social network analysis. The first one consists of maximizing the influence spread in a social network, starting with a given seed size of actors; the second one consists of minimizing the seed set to reach a given number of influenced nodes. Recently, both problems have been studied together with a multi-objective metaheuristic approach. In this work, diffusion filter restrictions based on the network topology are proposed to reduce the search space and thus improving the convergence speed of the solutions. This proposal allows increasing the quality of the results. As the influence spread model, the Linear Threshold model will be used. The solution is tested in three social networks of different sizes, finding promising improvements in harder instances.SOCIAL NETWORKINFLUENCE SPREAD MODELINFLUENCE MAXIMIZATIONMULTI-OBJECTIVE OPTIMIZATIONSWARM INTELLIGENCEArticulohttps://doi.org/10.1145/3487351.3488558