PaSta: Noisy Node Classification with Partial Label Learning

arXiv:2608.25365 · cs.LG · Submitted 2026-08-26 · Read on arXiv

cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Comments: 10 pages, 5 figures

Code: https://github.com/Yujingcn/PaSta-code

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Partial Label Learning — 30× in this paper
• Robustness — 12× in this paper · explained in 5 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• GNNs — 7× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Loss Function — 7× in this paper · explained in 2 episodes
• Pseudo-labels — 7× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Federated Learning — 3× in this paper · explained in 14 episodes
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Vote-Aggregated Cross-Entropy — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
• Pitfalls of Graph Neural Network Evaluation

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