SP-GCRL combines a nonlinear social diffusion model, dual-view contrastive learning for robust node embeddings, a GAT surrogate, and DDQN to learn end-to-end seed selection policies for influence maximization under partial graph observability.
ACM Computing Surveys55(5), 1–37 (2022)
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SP-GCRL: Influence Maximization on Incomplete Social Graphs
SP-GCRL combines a nonlinear social diffusion model, dual-view contrastive learning for robust node embeddings, a GAT surrogate, and DDQN to learn end-to-end seed selection policies for influence maximization under partial graph observability.