Summary of Cuq-gnn: Committee-based Graph Uncertainty Quantification Using Posterior Networks, by Clemens Damke et al.
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
by Clemens Damke, Eyke Hüllermeier
First submitted to arxiv on: 6 Sep 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 Graph Posterior Network (GPN) model estimates predictive uncertainty in node classification tasks by utilizing Normalizing Flows to compute class densities for each node. These densities are then converted into Dirichlet pseudo-counts, which are dispersed through the graph using personalized Page-Rank. The GPN architecture is motivated by three axioms on its uncertainty estimates, but these axioms may not always hold in practice. To address this, a family of Committee-based Uncertainty Quantification Graph Neural Networks (CUQ-GNNs) is introduced, combining standard Graph Neural Networks with Posterior Network-based uncertainty estimation. This approach adapts more flexibly to domain-specific demands on uncertainty estimates. The CUQ-GNN is compared to GPN and other approaches on common node classification benchmarks, showing its effectiveness in producing useful uncertainty estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how characteristics of a specific area can affect the way we measure uncertainty when making predictions about nodes in that area. Right now, there’s a model called Graph Posterior Network (GPN) that tries to do this by looking at each node individually and then spreading its results throughout the graph. The problem is that GPN doesn’t always follow some basic rules we have for how uncertainty should work. To fix this, the researchers came up with a new type of network that combines ideas from two other areas: standard Graph Neural Networks and Posterior Network-based methods. This new approach does a better job of adjusting to the specific needs of different areas. |
Keywords
» Artificial intelligence » Classification » Gnn
