TensorFLow or PyTorch? Both!

GraphGallery

GraphGallery is a gallery for benchmarking Graph Neural Networks (GNNs) and Graph Adversarial Learning with TensorFlow 2.x and PyTorch backend. Besides, Pytorch Geometric (PyG) backend and Deep Graph Library (DGL) backend now are available in GraphGallery.

💨 NEWS

We have integrated the Adversarial Attacks in this project, examples please refer to Graph Adversarial Learning examples.

🚀 Installation

pip install -U graphgallery

GraphGallery has been tested on:

• CPU, CUDA 10.1, CUDA 11.0
• TensorFlow 2.1~2.4, 2.1.2 is recommended.
• PyTorch 1.4~1.7
• Pytorch Geometric (PyG) 1.6.1
• DGL 0.5.2, 0.5.3

🤖 Implementations

Please refer to the examples directory. (The examples are updating...)

⚡ Quick Start on GNNs

Datasets

more details please refer to GraphData.

Example of GCN

from graphgallery.gallery import GCN

# initialize a GNN trainer
trainer = GCN(graph)
# process your inputs, such as converting to tensors
trainer.process()
# build your GCN trainer with default hyper-parameters
trainer.build()
# train your trainer. here splits.train_nodes and splits.val_nodes are numpy arrays
# verbose takes 0, 1, 2, 3, 4
history = trainer.train(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
# test your trainer
# verbose takes 0, 1, 2
results = trainer.test(splits.nodes, verbose=1)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')

Other models in the gallery are the same.

Using Other Backend

>>> import graphgallery
>>> graphgallery.backend()
TensorFlow 2.1.2 Backend

>>> graphgallery.set_backend("pytorch")
PyTorch 1.6.0+cu101 Backend

# DGL PyTorch backend
>>> graphgallery.set_backend("dgl")

# DGL TensorFlow backend
>>> graphgallery.set_backend("dgl-tf")

But your codes don't even need to change.

❓ How to add your datasets

This is motivated by gnn-benchmark

from graphgallery.data import Graph

# Load the adjacency matrix A, attribute matrix X and labels vector y
# A - scipy.sparse.csr_matrix of shape [num_nodes, num_nodes]
# X - scipy.sparse.csr_matrix or np.ndarray of shape [num_nodes, num_attrs]
# y - np.ndarray of shape [num_nodes]

mydataset = Graph(adj_matrix=A, node_attr=X, node_label=y)
# save dataset
mydataset.to_npz('path/to/mydataset.npz')
# load dataset
mydataset = Graph.from_npz('path/to/mydataset.npz')

⭐ Road Map

• Add PyTorch trainers support
• Add other frameworks (PyG and DGL) support
• Add more GNN trainers (TF and Torch backend)
• Support for more tasks, e.g., graph Classification and link prediction
• Support for more types of graphs, e.g., Heterogeneous graph
• Add Docstrings and Documentation (Building)
• Comprehensive tutorials

😘 Acknowledgement

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