Researchers have introduced SP-GCRL, a novel framework designed to tackle influence maximization challenges in social networks with incomplete data. The system employs a social-propagation-aware diffusion function to model complex user interactions and uses contrastive learning to generate robust node representations, even with missing connections. By integrating a graph attention network for efficient surrogate metric calculation and employing DDQN for policy learning, SP-GCRL demonstrates significant improvements over existing methods on real-world networks. AI
RANK_REASON The cluster contains a research paper published on arXiv detailing a new framework for influence maximization. [lever_c_demoted from research: ic=1 ai=1.0]
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