Real-time Targeted Influence Maximization with a Pre-training and Fine-tuning Framework

Influence maximization has recently gained substantial attention as a crucial aspect of scheduling online campaigns and advertisements on social network platforms. While existing methods have primarily focused on targeting influence, they tend to face challenges when dealing with frequent requests for different targeted advertisement instances. This results in a significant computational overhead when handling each Targeted Influence Maximization (TIM) instance separately. Motivated by this issue, we introduce a novel approach called the Pre-train and Fine-tune Framework. Our framework leverages prior data, specifically information from previously encountered TIM instances, to pre-train an initial solution set that can be rapidly adapted to new TIM instances with relatively low computational costs. This approach enables the generation of personalized solutions for each TIM instance, substantially reducing computational expenses when compared to optimizing the solution from scratch for every new TIM instance. The pre-training process involves solving a challenging discrete optimization problem, for which we introduce deterministic and probabilistic algorithms. Through extensive experiments conducted on real-world social networks, we demonstrate the effectiveness and efficiency of our proposed algorithms.