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.
Advances in neural information processing sys- tems30(2017)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
COAgents introduces a cooperative multi-agent system with a partial search graph to guide intensification and diversification in vehicle routing problems, achieving new state-of-the-art results among learning-based methods on VRPTW benchmarks.
citing papers explorer
-
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.
-
COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
COAgents introduces a cooperative multi-agent system with a partial search graph to guide intensification and diversification in vehicle routing problems, achieving new state-of-the-art results among learning-based methods on VRPTW benchmarks.