Summary of Generalization Of Graph Neural Networks Is Robust to Model Mismatch, by Zhiyang Wang and Juan Cervino and Alejandro Ribeiro
Generalization of Graph Neural Networks is Robust to Model Mismatch
by Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 graph neural networks (GNNs) that operate on geometric graphs generated from manifold models, specifically focusing on scenarios where there is a mismatch between the training and testing data. The authors analyze GNN generalization in the presence of this model mismatch and find that GNNs trained on graphs generated from a manifold can still generalize well to unseen nodes and graphs generated from a mismatched manifold. The study reveals that the generalization gap decreases as the number of nodes grows in the training graph, but increases with larger manifold dimension and larger mismatch. The findings also indicate a trade-off between GNN generalization and the ability to discriminate high-frequency components when facing a model mismatch. This analysis sheds light on the design of filter designs for generalizable GNNs robust to model mismatch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well graph neural networks (GNNs) work in situations where the data used to train them is different from the data they’re tested with. The authors found that even when there’s a big difference between the training and testing data, GNNs can still be good at making predictions. They also discovered that the size of the training graph and the dimension of the manifold model both affect how well GNNs generalize. This is important because it means we need to think about how to design GNN filters so they’re robust to these kinds of changes. |
Keywords
» Artificial intelligence » Generalization » Gnn
