https://github.com/graph-com/bayesian_inference_based_gnn
[Neurips2022] Understanding Non-linearity in Graph Neural Networks from the Perspective of Bayesian Inference
https://github.com/graph-com/bayesian_inference_based_gnn
Last synced: 4 months ago
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[Neurips2022] Understanding Non-linearity in Graph Neural Networks from the Perspective of Bayesian Inference
- Host: GitHub
- URL: https://github.com/graph-com/bayesian_inference_based_gnn
- Owner: Graph-COM
- Created: 2022-10-11T23:52:28.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-12T04:10:03.000Z (over 3 years ago)
- Last Synced: 2025-04-06T05:51:12.346Z (10 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 20.5 KB
- Stars: 7
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NeurIPS2022 "Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective"
By Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li
## Introduction
Graph neural networks (GNNs) have become the de-facto standard used in many graph learning tasks due to their super empirical performance. Researchers often attribute such success to non-linearity in GNNs which associates them with great expressive power. However, for node classification tasks, many studies have shown that non-linearity to control the exchange of features among neighbors seems not that crucial. In this work, we resort to understand the effect of non-linearity by comparing with the linear counterparts for node classification tasks from a Bayesian Inference perspective.
## Main Results
* When the node attributes are less informative compared to the structural information, non-linear
propagation and linear propagation have almost the same mis-classification error.
* When the node attributes are more informative, non-linear propagation shows advantages. The
mis-classification error of non-linear propagation can be significantly smaller than that of linear
propagation with sufficiently informative node attributes.
* When there is a distribution shift of the node attributes between the training and testing datasets,
non-linearity provides better transferability in the regime of informative node attributes.
## Run Real Dataset Examples
We provide examples with minimal code to run real dataset experiments in `./Neurips2022_Understanding_Non_linearity_in_Graph_Neural_Networks_from_the_Bayesian_Inference_Perspective_Experiments.ipynb`, which includes experiemnts on PubMed, Cora, Citeseer under both Gaussian and Laplacian assumptions.
**Colab:** 
to play with `Neurips2022_Understanding_Non_linearity_in_Graph_Neural_Networks_from_the_Bayesian_Inference_Perspective_Experiments.ipynb` in Colab.
## Reference
If you find our paper and repo useful, please cite our paper:
```bibtex
@article{wei2022understanding,
title={Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective},
author={Wei, Rongzhe and Yin, Haoteng and Jia, Junteng and Benson, Austin R and Li, Pan},
journal={Advances in Neural Information Processing Systems},
year={2022}
}
```