Enhanced Group Influence Maximization in Social Networks Using Deep Reinforcement Learning

In contemporary society, groups are pivotal in shaping decisions and actions. The consensus of a majority of members on specific topics often guides the collective decision-making in groups. Group influence maximization (GIM) aims to select $k$ seed users in a network to maximize the number of eventually activated groups. A group is said to be activated if $\beta$ percent of users in this group are activated. This study delves into the strategic selection of seed users in social networks to maximize the spread of a topic, thereby activating the highest number of groups. The GIM problem, inherently NP-hard when computing the influence spread from a selected set of nodes, has traditionally faced obstacles in ensuring theoretical robustness, time efficiency, and adaptability in large and complex network environments. To overcome these challenges, we introduce a robust framework called GIMDRL that addresses the GIM problem in social networks using deep reinforcement learning (DRL). Our approach integrates node embeddings from multiple graph neural networks, thereby utilizing diverse information for effective network analysis. This integration plays a crucial role in optimizing the parameter learning process. Extensive experiments are conducted on real-world and synthetic datasets to assess the performance of our proposed framework. The results of these experiments indicate that our approach significantly outperforms existing methods in GIM, even when trained on sampled graphs. This highlights our model's strong capacity for generalization in varying network scenarios.