HIA-GAT, a heterogeneous graph attention network with conflict-type-aware gating, reports the highest AUC for frame-level risk prediction on NGSIM I-80 and US-101 datasets, with largest gains on lateral (PET) conflicts.
Heterogeneous edge-enhanced graph attention network for multi-agent trajectory prediction
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Develops a heterogeneous GNN workflow on HydraGNN for large-scale OPF surrogate modeling across varied grid topologies and shows that pretraining improves fine-tuning on feasibility and N-1 contingency tasks.
citing papers explorer
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hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways
HIA-GAT, a heterogeneous graph attention network with conflict-type-aware gating, reports the highest AUC for frame-level risk prediction on NGSIM I-80 and US-101 datasets, with largest gains on lateral (PET) conflicts.
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Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Develops a heterogeneous GNN workflow on HydraGNN for large-scale OPF surrogate modeling across varied grid topologies and shows that pretraining improves fine-tuning on feasibility and N-1 contingency tasks.