arXiv:2605.12513v2 Announce Type: replace-cross 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 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 obt
The development of SP-GCRL reflects ongoing advancements in AI, particularly in reinforcement learning and graph neural networks, addressing real-world complexities in social network analysis.
This framework offers a critical breakthrough in influence maximization under realistic conditions, enabling more effective targeting and strategy in various domains, from marketing to public health campaigns.
The ability to accurately model and optimize influence on incomplete and dynamic social graphs changes how organizations can approach targeted communication and viral spread, making traditional methods less effective.
- · AI/ML researchers
- · Social media platforms
- · Digital marketers
- · Public health organizations
- · Organizations relying on naive influence models
- · Manual social network analysts
Improved efficiency and accuracy of influence campaigns on social platforms.
Increased ability for malicious actors to spread misinformation or manipulate opinions if similar tools are misused.
Potential for new ethical and regulatory challenges concerning algorithmic influence and user autonomy on social networks.
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Read at arXiv cs.AI