Abstract: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 this http URL 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.
| Comments: | Accepted by DASFAA 2026. The first two authors contributed equally |
| Subjects: | Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.12513 [cs.SI] |
| (or arXiv:2605.12513v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12513 arXiv-issued DOI via DataCite |
Submission history
From: Haohua Niu [view email]
[v1]
Tue, 31 Mar 2026 09:44:20 UTC (1,143 KB)