Metadata-Version: 2.1
Name: graphgallery
Version: 0.1.6
Summary: Geometric Deep Learning Extension Library for TensorFlow
Home-page: https://github.com/EdisonLeeeee/Graphgallery
Author: Jintang Li
Author-email: cnljt@outlook.com
License: UNKNOWN
Download-URL: https://github.com/EdisonLeeeee/Graphgallery/archive/0.1.6.tar.gz
Keywords: tensorflow,geometric-deep-learning,graph-neural-networks
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: networkx (==2.3)
Requires-Dist: metis (==0.2a4)
Requires-Dist: scipy (==1.4.1)
Requires-Dist: tensorflow (>=2.1.0)
Requires-Dist: numpy (>=1.17.4)
Requires-Dist: gensim (>=3.8.0)
Requires-Dist: texttable (>=1.6.2)
Requires-Dist: numba (>=0.46.0)
Requires-Dist: tqdm (>=4.40.2)
Requires-Dist: scikit-learn (>=0.23.1)

# GraphGallery

[pypi-image]: https://badge.fury.io/py/graphgallery.svg
[pypi-url]: https://pypi.org/project/graphgallery/

<p align="center">
  <img width = "500" height = "300" src="https://github.com/EdisonLeeeee/Graphgallery/blob/master/imgs/graphgallery.svg" alt="logo"/>
</p>

---

[![PyPI Version][pypi-image]][pypi-url]


A gallery of state-of-the-arts graph neural networks. Implemented with [TensorFlow](https://github.com/tensorflow/tensorflow) 2.x.

This repo aims to achieve 4 goals:
+ Similar or higher performance
+ Faster training and testing
+ Simple and convenient to use, high scalability
+ Easy to read source codes

# Requirements

+ python>=3.6
+ tensorflow>=2.1 (2.1 is recommended)
+ networkx==2.3
+ scipy>=1.4.1
+ scikit_learn>=0.22
+ numpy>=1.17.4
+ numba>=0.48
+ gensim>=3.8.1

Other packages (not necessary）:

+ metis==0.2a4 (required for `ClusterGCN`)
+ texttable

# Install
```bash
pip install -U graphgallery
```

# Implementation
## General models
+ **GCN** from *Semi-Supervised Classification with Graph Convolutional Networks* [📝Paper](https://arxiv.org/abs/1609.02907)
+ **GAT** from *Graph Attention Networks* [📝Paper](https://arxiv.org/abs/1710.10903>)
+ **SGC** from *Simplifying Graph Convolutional Networks* [📝Paper](https://arxiv.org/abs/1902.07153)
+ **GraphSAGE** from *Inductive Representation Learning on Large Graphs* [📝Paper](https://arxiv.org/abs/1706.02216)
+ **GWNN** from *Graph Wavelet Neural Network* [📝Paper](https://arxiv.org/abs/1904.07785)
+ **GMNN** from *Graph Markov Neural Networks* [📝Paper](https://arxiv.org/abs/1905.06214>)
+ **ChebyNet** from *Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering* [📝Paper](https://arxiv.org/abs/1606.09375>)
+ **ClusterGCN** from *Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks* [📝Paper](https://arxiv.org/abs/1905.07953)
+ **FastGCN** from *FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling* [📝Paper](https://arxiv.org/abs/1801.10247)
+ **LGCN** from  *Large-Scale Learnable Graph Convolutional Networks* [📝Paper](https://arxiv.org/abs/1808.03965)

## Defense models
+ **RobustGCN** from *Robust Graph Convolutional Networks Against Adversarial Attacks* [📝Paper](https://dl.acm.org/doi/10.1145/3292500.3330851)
+ **SBVAT/OBVAT** from *Batch Virtual Adversarial Training for Graph Convolutional Networks* [📝Paper](https://arxiv.org/abs/1902.09192)

## Other custom models
+ **GCN_MIX**: Mixture of GCN and MLP
+ **GCNF**: GCN + feature
+ **DenseGCN**: Dense version of GCN
+ **EdgeGCN**: GCN using message passing framework
+ **MedianSAGE**: GraphSAGE using `Median` aggregation

# Quick Start
## Example of GCN model
```python
from graphgallery.nn.models import GCN
# adj is scipy sparse matrix, x is numpy array matrix
model = GCN(adj, x, labels, device='GPU', seed=123)
# build your GCN model with custom hyper-parameters
model.build()
# train your model. here idx_train and idx_val are numpy arrays
his = model.train(idx_train, idx_val, verbose=1, epochs=100)
# test your model
loss, accuracy = model.test(idx_test)
print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
```
On `Cora` dataset:
```
loss 1.02, acc 95.00%, val_loss 1.41, val_acc 77.40%: 100%|██████████| 100/100 [00:02<00:00, 37.07it/s]
Test loss 1.4123, Test accuracy 81.20%
```

## Build your model
you can use the following statement to build your model
```python
# one hidden layer with hidden units 32 and activation function RELU
>>> model.build(hiddens=32, activations='relu')

# two hidden layer with hidden units 32, 64 and all activation functions are RELU
>>> model.build(hiddens=[32, 64], activations='relu')

# two hidden layer with hidden units 32, 64 and activation functions RELU and ELU
>>> model.build(hiddens=[32, 64], activations=['relu', 'elu'])

# other parameters like `dropouts` and `l2_norms` (if have) are the SAME.
```
## Train or test your model
More details can be seen in the methods [model.train](https://github.com/EdisonLeeeee/Graphgallery/blob/master/graphgallery/nn/models/supervised/supervised_model.py#L80) and [model.test](https://github.com/EdisonLeeeee/Graphgallery/blob/master/graphgallery/nn/models/supervised/supervised_model.py#L382) 

## Hyper-parameters
you can simply use `model.show()` to show all your `Hyper-parameters`.
Otherwise you can also use `model.show('model')` or `model.show('train')` to show your model parameters and training parameters.
NOTE: you should install texttable first.

## Visualization
+ Accuracy
```python
import matplotlib.pyplot as plt
plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.legend(['Accuracy', 'Val Accuracy'])
plt.xlabel('Epochs')
plt.show()
```
![visualization](https://github.com/EdisonLeeeee/Graphgallery/blob/master/imgs/visualization_acc.png)

+ Loss
```python
import matplotlib.pyplot as plt
plt.plot(his.history['loss'])
plt.plot(his.history['val_loss'])
plt.legend(['Loss', 'Val Loss'])
plt.xlabel('Epochs')
plt.show()
```
![visualization](https://github.com/EdisonLeeeee/Graphgallery/blob/master/imgs/visualization_loss.png)




