SP-GCRL: Influence Maximization on Incomplete Social Graphs
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918498247114752 |
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| author | Niu, Haohua Yang, Yuxuan Zhang, Lingfeng Li, Hao Liang, Jiao Luo, Zongfu Rossi, Luca |
| author_facet | Niu, Haohua Yang, Yuxuan Zhang, Lingfeng Li, Hao Liang, Jiao Luo, Zongfu Rossi, Luca |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_12513 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SP-GCRL: Influence Maximization on Incomplete Social Graphs Niu, Haohua Yang, Yuxuan Zhang, Lingfeng Li, Hao Liang, Jiao Luo, Zongfu Rossi, Luca Social and Information Networks Artificial Intelligence 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. |
| title | SP-GCRL: Influence Maximization on Incomplete Social Graphs |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2605.12513 |