Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics. We propose SP-GCRL, a social-propagation-aware graph contrastive reinforcement learning framework that learns end-to-end seed selection under partial observability.We first introduce a social-propagation-aware nonlinear diffusion function to model reinforcement/diminishing effects and probability drift under repeated exposure; we then construct dual structural views and perform contrastive learning to obtain node representations robust to missing edges and weak ties, while replacing expensive strategy metrics with a GAT-based regression surrogate to improve efficiency and scalability; finally, we use DDQN to learn an end-to-end seed selection policy on top of these representations. Experiments on multiple real-world networks show that SP-GCRL achieves significant gains over heuristic and learning-based baselines across budgets and topologies, while maintaining strong large-scale scalability.

翻译：现实平台中的影响力最大化面临不完整、含噪声的社交图以及非平稳扩散动态的挑战。我们提出SP-GCRL，一种社交传播感知的图对比强化学习框架，可在部分可观测条件下学习端到端的种子节点选择。我们首先引入一种社交传播感知的非线性扩散函数，用于建模重复暴露下的增强/衰减效应和概率漂移；接着构建双重视角结构并进行对比学习，以获得对缺失边和弱连接鲁棒的节点表示，同时用基于GAT的回归代理替代昂贵的策略指标，以提高效率和可扩展性；最后，我们使用DDQN在这些表示上学习端到端的种子选择策略。在多个真实网络上的实验表明，SP-GCRL在不同预算和拓扑结构下均优于启发式和基于学习的基线方法，同时保持了强大规模可扩展性。