Understanding the mechanisms of diffusion of behaviors in social groups represents a major challenge in social science. The Linear Threshold Model (LTM) on graphs provides a useful framework, modelling each individual as a node that adopts a behavior if the influence from active neighbours exceeds a personal threshold. In this thesis we address the problem faced by a social planner aiming to induce complete adoption through propagation while minimizing intervention costs. We analyze two intervention strategies: targeting, which selects individuals for direct activation, and partial incentive, which reduces thresholds to ease diffusion. Although both problems are NP-hard on arbitrary graphs, we prove that specific topologies admit polynomial-time exact algorithms.