A triangle-message GNN for multicut outperforms heuristics in solution quality on graphs up to 200 nodes and finds optimal solutions faster than exact solvers for some cases.
The Graph Neural Network Model , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces TEDBench benchmark and MiAE self-supervised framework that outperforms baselines for large-scale protein fold classification.
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.
citing papers explorer
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Graph Neural Networks with Triangle-Based Messages for the Multicut Problem
A triangle-message GNN for multicut outperforms heuristics in solution quality on graphs up to 200 nodes and finds optimal solutions faster than exact solvers for some cases.
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Protein Fold Classification at Scale: Benchmarking and Pretraining
Introduces TEDBench benchmark and MiAE self-supervised framework that outperforms baselines for large-scale protein fold classification.
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Mesh Based Simulations with Spatial and Temporal awareness
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.