RAwR augments graphs with role-aware quotient graphs from approximate equitable partitions to accelerate long-range communication in GNNs, achieving SOTA results on homophilic, heterophilic, and long-range benchmarks while recovering master-node rewiring in the limit.
IEEE transactions on neural networks , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
citing papers explorer
-
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
RAwR augments graphs with role-aware quotient graphs from approximate equitable partitions to accelerate long-range communication in GNNs, achieving SOTA results on homophilic, heterophilic, and long-range benchmarks while recovering master-node rewiring in the limit.
-
Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.
-
Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.