Summary of Towards Precise Prediction Uncertainty in Gnns: Refining Gnns with Topology-grouping Strategy, by Hyunjin Seo et al.
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
by Hyunjin Seo, Kyusung Seo, Joonhyung Park, Eunho Yang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper investigates the calibration of graph neural networks (GNNs) and proposes a novel approach called Simi-Mailbox that categorizes nodes based on both neighborhood similarity and confidence levels. The authors analyze existing methods and find that they do not universally apply, as calibration errors can differ significantly among nodes with similar neighborhood predictions. Instead, Simi-Mailbox employs group-specific temperature scaling to address specific miscalibration levels of affiliated nodes. The approach is evaluated across diverse datasets and GNN architectures, achieving up to 13.79% error reduction compared to uncalibrated GNN predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make graph neural networks (GNNs) more accurate. Right now, we have ways to make them better, but they don’t always work the same way for all nodes in a graph. The authors came up with a new idea called Simi-Mailbox that looks at both where each node is connected and how confident it is about its predictions. This helps the GNNs get even more accurate by adjusting its “temperature” (a measure of confidence) separately for different groups of connected nodes. The results show that this approach can make the GNNs up to 13.79% better than before. |
Keywords
» Artificial intelligence » Gnn » Temperature
