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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
Towards Trustworthy Hypergraph Neural Networks under Label Noise
TL;DR
HyperTrust, a new framework for hypergraph neural networks, effectively addresses label noise by estimating hyperedge trustworthiness and enhancing supervision. Extensive experiments show its robustness across various datasets.
✦ Why It Matters
Implement HyperTrust to improve the robustness of your hypergraph neural network models against label noise today.
Key Takeaways
- Explore HyperTrust for robust hypergraph learning under label noise.
- Utilize HyperedgeBoost to enhance supervision in your models.
- Apply HyperedgePrune to reduce the impact of noisy labels.
- Benchmark your hypergraph models against the proposed unified standards.
How It Works
HyperTrust operates by first assessing the trustworthiness of hyperedges using a pretraining-based, entropy-aware strategy. It then employs the HyperedgeBoost module to link unlabeled nodes to reliable hyperedges, enhancing the quality of supervision.
Simultaneously, the HyperedgePrune module identifies and removes untrustworthy node-hyperedge connections, effectively curtailing the spread of label noise. This dual approach allows HyperTrust to adjust the hypergraph structure dynamically, leading to more accurate predictions.