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A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
| Date | Stars |
|---|---|
| 2026-07-24 | 607 |
| 2026-07-25 | 607 |
| 2026-07-28 | 607 |
| 2026-07-30 | 607 |
| 2026-07-31 | 607 |
| 2026-08-06 | 607 |
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Graph Wavelet Neural Network
================================
[](https://arxiv.org/abs/1904.07785) [](https://codebeat.co/projects/github-com-benedekrozemberczki-graphwaveletneuralnetwork-master) [](https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork/archive/master.zip) [](https://twitter.com/intent/follow?screen_name=benrozemberczki)
A **PyTorch** implementation of **Graph Wavelet Neural Network (ICLR 2019).**
<div style="text-align:center"><img src ="gwnn.jpg" ,width=720/></div>
### Abstract
<p align="justify">
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.</p>
A reference Tensorflow implementation is accessible [[here]](https://github.com/Eilene/GWNN).
This repository provides an implementation of Graph Wavelet Neural Network as described in the paper:
> Graph Wavelet Neural Network.
> Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng.
> ICLR, 2019.
> [[Paper]](https://openreview.net/forum?id=H1ewdiR5tQ)
-------------------------------------------
### Requirements
The codebase is implemented in Python 3.5.2. package versions used for development are just below.
```
networkx 2.4
tqdm 4.28.1
numpy 1.15.4
pandas 0.23.4
texttable 1.5.0
scipy 1.1.0
argparse 1.1.0
torch 1.1.0
torch-scatter 1.4.0
torch-sparse 0.4.3
torch-cluster 1.4.5
torch-geometric 1.3.2
torchvision 0.3.0
scikit-learn 0.20.0
PyGSP 0.5.1
```
### Datasets
<p align="justify">
The code takes the **edge list** of the graph in a csv file. Every row indicates an edge between two nodes separated by a comma. The first row is a header. Nodes should be indexed starting with 0. A sample graph for `Cora` is included in the `input/` directory. In addition to the edgelist there is a JSON file with the sparse features and a csv with the target variable.</p>
<p align="justify">
The **feature matrix** is a sparse binary one it is stored as a json. Nodes are keys of the json and feature indices are the values. For each node feature column ids are stored as elements of a list. The feature matrix is structured as:</p>
```javascript
{ 0: [0, 1, 38, 1968, 2000, 52727],
1: [10000, 20, 3],
2: [],
...
n: [2018, 10000]}
```
<p align="justify">
The **target vector** is a csv with two columns and headers, the first contains the node identifiers the second the targets. This csv is sorted by node identifiers and the target column contains the class meberships indexed from zero. </p>
| **NODE ID**| **Target** |
| --- | --- |
| 0 | 3 |
| 1 | 1 |
| 2 | 0 |
| 3 | 1 |
| ... | ... |
| n | 3 |
### Options
Training the model is handled by the `src/main.py` script which provides the following command line arguments.
#### Input and output options
```
--edge-path STR Input graph path. Default is `input/cora_edges.csv`.
--features-path STR Features path. Default is `input/cora_features.Excerpt of 5,896 characters
Read on GitHub
Benedek Rozemberczki · @google · United Kingdom
118
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6821ccf0794c45ae, topic:deep-learning, topic:pytorch, topic:tensorflow