Summary of Energy-based Epistemic Uncertainty For Graph Neural Networks, by Dominik Fuchsgruber et al.
Energy-based Epistemic Uncertainty for Graph Neural Networks
by Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, called GEBM, is an energy-based model that provides high-quality uncertainty estimates for Graph Neural Networks (GNNs) in domains with interdependent data. Unlike existing techniques, GEBM aggregates energy at different structural levels to quantify epistemic uncertainty, which arises from graph diffusion. This leads to improved predictive robustness and separation of in-distribution and out-of-distribution data on various anomaly types. The framework is a simple and effective post-hoc method applicable to any pre-trained GNN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GEBM is a new way to make Graph Neural Networks more reliable. It helps GNNs understand how sure they are about their predictions, which is important when dealing with complex graph data. Currently, GNNs don’t do this very well, so GEBM fills an important gap. The method works by looking at the structure of the graph and combining different types of uncertainty into one measure. This makes it better than other methods that only consider one type of uncertainty. GEBM is simple to use and can be applied to any pre-trained GNN. |
Keywords
» Artificial intelligence » Diffusion » Energy based model » Gnn
