graphgallery 0.1.6

Geometric Deep Learning Extension Library for TensorFlow

GraphGallery

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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>=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

pip install -U graphgallery

Implementation

General models

• GCN from Semi-Supervised Classification with Graph Convolutional Networks 📝Paper
• GAT from Graph Attention Networks 📝Paper
• SGC from Simplifying Graph Convolutional Networks 📝Paper
• GraphSAGE from Inductive Representation Learning on Large Graphs 📝Paper
• GWNN from Graph Wavelet Neural Network 📝Paper
• GMNN from Graph Markov Neural Networks 📝Paper
• ChebyNet from Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering 📝Paper
• ClusterGCN from Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks 📝Paper
• FastGCN from FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling 📝Paper
• LGCN from Large-Scale Learnable Graph Convolutional Networks 📝Paper

Defense models

• RobustGCN from Robust Graph Convolutional Networks Against Adversarial Attacks 📝Paper
• SBVAT/OBVAT from Batch Virtual Adversarial Training for Graph Convolutional Networks 📝Paper

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

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

# 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

• Accuracy

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()

• Loss

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()