Summary of Asymptotic Generalization Error Of a Single-layer Graph Convolutional Network, by O. Duranthon et al.
Asymptotic generalization error of a single-layer graph convolutional network
by O. Duranthon, L. Zdeborová
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn)
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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 paper investigates the generalization properties of graph convolutional networks (GCNs) as a function of the number of samples. By analyzing the performance of a single-layer GCN trained on data generated by attributed stochastic block models (SBMs), the authors predict the performances in the high-dimensional limit. They generalize previous work on ridge regression for contextual-SBM and add analysis for neural-prior SBM, also exploring the high signal-to-noise ratio limit. The study shows that while consistent, GCNs do not reach the Bayes-optimal rate for any of the considered cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph convolutional networks are a type of machine learning model that’s great at processing graph data. But scientists don’t fully understand how well they work when we have a lot of data. In this study, researchers looked at how these models do when trained on special kinds of data called attributed stochastic block models. They wanted to see what happens when there’s lots of information and also when the signal is really strong. The results show that these models are consistent but don’t quite reach their full potential. |
Keywords
* Artificial intelligence * Gcn * Generalization * Machine learning * Regression
