Direct search methods for influence maximization problems

The influence maximization problem seeks to maximize the spread of influence in network structures. This challenging optimization task remains unsolved due to its combinatorial nature and computational complexity. In this thesis, we propose two novel direct search methods, Neighbors Search (NS) and nonmonotone NS, which leverage the network structure to enhance efficiency. We compare these methods with the state-of-the-art Custom Direct Search (CDS) method through experiments on artificial and real-world networks. Our findings show promising improvements in solving the influence maximization problem.