解决社交图不完整下的影响力最大化问题，端到端学习种子节点选择。
SP-GCRL: Influence Maximization on Incomplete Social Graphs
- 设计非线性传播函数，捕捉重复曝光的增强与衰减效应。
- 对比学习构建双结构视图，提升对缺失边和弱连接的鲁棒性。
- 结合GAT回归与DDQN，实现高效可扩展的端到端策略学习。
真实平台中的影响力最大化面临社交图不完整、噪声多及扩散动态非平稳的挑战。本文提出SP-GCRL框架，一种面向社会传播的图对比强化学习方法，可在部分可观测条件下端到端学习种子节点选择。首先引入社会传播感知的非线性扩散函数，建模重复曝光带来的增强与衰减效应及概率漂移；接着构建双结构视图并进行对比学习，获得对缺失边和弱连接鲁棒的节点表示；同时用GAT-based回归代理替代昂贵的策略度量，提升效率与可扩展性；最后基于这些表示使用DDQN学习端到端种子选择策略。在多个真实网络上的实验表明，SP-GCRL在不同预算和拓扑下均显著优于启发式与学习基线方法，且具备强大规模可扩展性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。