Summary of In-n-out: Calibrating Graph Neural Networks For Link Prediction, by Erik Nascimento et al.
In-n-Out: Calibrating Graph Neural Networks for Link Prediction
by Erik Nascimento, Diego Mesquita, Samuel Kaski, Amauri H Souza
First submitted to arxiv on: 7 Mar 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 tackles a crucial issue in graph neural networks (GNNs) – their miscalibration when predicting links. Unlike node-level classification, where GNNs tend to be overconfident, they exhibit mixed behavior for link prediction. IN-N-OUT is proposed as the first method to calibrate GNNs for this task, leveraging two intuitions: small fluctuations in embeddings for true/false labels and more substantial changes when labeling edges contradicting GNN predictions. The method is experimentally evaluated, outperforming available baselines designed for node-level classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well computer models called graph neural networks (GNNs) can predict relationships between things. GNNs are good at understanding individual things, but they’re not very good at predicting whether those things will have a connection or not. The authors of this paper found that GNNs often make mistakes when predicting these connections, either being too confident or not confident enough. They came up with a new way to fix this problem called IN-N-OUT, which helps GNNs make more accurate predictions about connections between things. |
Keywords
* Artificial intelligence * Classification * Gnn
