GraphGallery

A gallery of state-of-the-arts graph neural networks. Implemented with 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
• scikit_learn
• numpy
• numba
• gensim

Other packages (not necessary):

• metis==0.2a4 (required for ClusterGCN)
• texttable

Installation

pip install -U graphgallery

Implementation

General models

• ChebyNet from Michaël Defferrard et al, 📝Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS'16, :octocat:Codes
• GCN from Thomas N. Kipf et al, 📝Semi-Supervised Classification with Graph Convolutional Networks, ICLR'17, :octocat:Codes
• GraphSAGE from William L. Hamilton et al, 📝Inductive Representation Learning on Large Graphs, NIPS'17, :octocat:Codes
• FastGCN from Jie Chen et al, FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling , ICLR'18,:octocat:Codes
• LGCN from Hongyang Gao et al, 📝Large-Scale Learnable Graph Convolutional Networks, KDD'18, :octocat:Codes
• GAT from Petar Veličković et al, 📝Graph Attention Networks, ICLR'18, :octocat:Codes
• SGC from Felix Wu et al, 📝Simplifying Graph Convolutional Networks, ICML'19, :octocat:Codes
• GWNN from Bingbing Xu et al, 📝Graph Wavelet Neural Network, ICLR'19,:octocat:Codes
• GMNN from Meng Qu et al, 📝Graph Markov Neural Networks, ICML'19,:octocat:Codes
• ClusterGCN from Wei-Lin Chiang et al, 📝Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks, KDD'19, :octocat:Codes
• DAGNN from Meng Liu et al, 📝Towards Deeper Graph Neural Networks, KDD'20, :octocat:Codes

Defense models

• RobustGCN from Dingyuan Zhu et al, 📝Robust Graph Convolutional Networks Against Adversarial Attacks, KDD'19, :octocat:Codes
• SBVAT/OBVAT from Zhijie Deng et al, 📝Batch Virtual Adversarial Training for Graph Convolutional Networks, ICML'19, :octocat:Codes

Quick Start

Example of GCN model

from graphgallery.nn.models import GCN
# adj is scipy sparse matrix, x is numpy array matrix
model = GCN(adj, x, labels, device='GPU', norm_x='l1', 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

# 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 and model.test

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

NOTE: you must install SciencePlots package for a better preview.

• Accuracy

import matplotlib.pyplot as plt
with plt.style.context(['science', 'no-latex']):
    plt.plot(his.history['acc'])
    plt.plot(his.history['val_acc'])
    plt.legend(['Train Accuracy', 'Val Accuracy'])
    plt.ylabel('Accuracy')
    plt.xlabel('Epochs')
    plt.autoscale(tight=True)
    plt.show()    

• Loss

import matplotlib.pyplot as plt
with plt.style.context(['science', 'no-latex']):
    plt.plot(his.history['loss'])
    plt.plot(his.history['val_loss'])
    plt.legend(['Train Loss', 'Val Loss'])
    plt.ylabel('Loss')
    plt.xlabel('Epochs')
    plt.autoscale(tight=True)
    plt.show()    

Acknowledgement

This project is motivated by Pytorch Geometric, Tensorflow Geometric and Stellargraph, and the original implementations from the authors, thanks for their excellent works!