TensorFLow or PyTorch? Both!
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.
We have integrated the Adversarial Attacks in this project, examples please refer to Graph Adversarial Learning examples.
pip install -U graphgalleryor
git clone https://github.com/EdisonLeeeee/GraphGallery.git
cd GraphGallery
pip install -e .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
In detail, the following methods are currently implemented:
ChebyNet from Michaël Defferrard et al, 📝Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (NeurIPS'16)
[:octocat:TensorFLow] [🔥PyTorch]GCN from Thomas N. Kipf et al, 📝Semi-Supervised Classification with Graph Convolutional Networks (ICLR'17)
[:octocat:TensorFLow] [🔥PyTorch] [🔥PyG] [🔥DGL-PyTorch] [:octocat:DGL-TensorFlow]GraphSAGE from William L. Hamilton et al, 📝Inductive Representation Learning on Large Graphs (NeurIPS'17)
[:octocat:TensorFLow] [🔥PyTorch]FastGCN from Jie Chen et al, 📝FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling (ICLR'18)
[:octocat:TensorFLow] [🔥PyTorch]LGCN from Hongyang Gao et al, 📝Large-Scale Learnable Graph Convolutional Networks (KDD'18)
[:octocat:TensorFLow] [🔥PyTorch]GAT from Petar Veličković et al, 📝Graph Attention Networks (ICLR'18)
[:octocat:TensorFLow] [🔥PyTorch] [🔥PyG]SGC from Felix Wu et al, 📝Simplifying Graph Convolutional Networks (ICLR'19)
[:octocat:TensorFLow] [🔥PyTorch] [🔥PyG]GWNN from Bingbing Xu et al, 📝Graph Wavelet Neural Network (ICLR'19)
[:octocat:TensorFLow] [🔥PyTorch]ClusterGCN from Wei-Lin Chiang et al, 📝Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (KDD'19)
[:octocat:TensorFLow] [🔥PyTorch] [🔥PyG]DAGNN from Meng Liu et al, 📝Towards Deeper Graph Neural Networks (KDD'20)
[:octocat:TensorFLow] [🔥PyTorch]GDC from Johannes Klicpera et al, 📝Diffusion Improves Graph Learning (NeurIPS'19)
[:octocat:TensorFLow] [🔥PyTorch] [🔥PyG]TAGCN from Du et al, 📝Topology Adaptive Graph Convolutional Networks (arxiv'17)
[:octocat:TensorFLow] [🔥PyTorch]APPNP, PPNP from Johannes Klicpera et al, 📝Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR'19)
[:octocat:TensorFLow(APPNP)] [:octocat:TensorFLow(PPNP)] [🔥PyTorch(APPNP)] [🔥PyTorch(PPNP)]PDN from Benedek Rozemberczki et al, 📝Pathfinder Discovery Networks for Neural Message Passing (ICLR'21)
[🔥PyG]AGNN from Zhu et al, 📝Attention-based Graph Neural Network for semi-supervised learning (ICLR'18 openreview)
[:octocat:TensorFLow] [🔥PyTorch]ARMA from Bianchi et al., 📝Graph Neural Networks with convolutional ARMA filters (Arxiv'19)
[:octocat:TensorFLow] [🔥PyTorch]RobustGCN from Petar Veličković et al, 📝Robust Graph Convolutional Networks Against Adversarial Attacks (KDD'19)
[:octocat:TensorFLow] [🔥PyTorch]SBVAT from Zhijie Deng et al, 📝Batch Virtual Adversarial Training for Graph Convolutional Networks (ICML'19)
[:octocat:TensorFLow] [🔥PyTorch]OBVAT from Zhijie Deng et al, 📝Batch Virtual Adversarial Training for Graph Convolutional Networks (ICML'19)
[:octocat:TensorFLow] [🔥PyTorch]SimPGCN from Wei Jin et al, 📝Node Similarity Preserving Graph Convolutional Networks (WSDM'21)
[🔥PyTorch]The graph purification methods are universal for all models, just specify:
graph_transform="purification_method"so, here we only give the examples of GCN with purification methods, other models should work.
GCN-Jaccard from Huijun Wu et al, 📝Adversarial Examples on Graph Data: Deep Insights into Attack and Defense (IJCAI'19)
[:octocat:TensorFLow] [🔥PyTorch]GCN-SVD from Negin Entezari et al, 📝All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs (WSDM'20)
[:octocat:TensorFLow] [🔥PyTorch]Deepwalk from Bryan Perozzi et al, 📝DeepWalk: Online Learning of Social Representations (KDD'14)
[Example]Node2vec from Aditya Grover and Jure Leskovec, 📝node2vec: Scalable Feature Learning for Networks (KDD'16)
[Example]more details please refer to GraphData.
It takes just a few lines of code.
from graphgallery.gallery.nodeclas import GCN
trainer = GCN()
trainer.make_data(graph)
trainer.build()
history = trainer.fit(train_nodes, val_nodes)
results = trainer.evaluate(test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')Other models in the gallery are the same.
>>> import graphgallery
# Default: TensorFlow backend
>>> graphgallery.backend()
TensorFlow 2.1.2 Backend
# Switch to PyTorch backend
>>> graphgallery.set_backend("torch")
# Switch to TensorFlow backend
>>> graphgallery.set_backend("tf")
# Switch to PyTorch Geometric backend
>>> graphgallery.set_backend("pyg")
# Switch to DGL PyTorch backend
>>> graphgallery.set_backend("dgl")
# Switch to DGL TensorFlow backend
>>> graphgallery.set_backend("dgl-tf")But your codes don't even need to change.
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')- 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 Classificationandlink prediction - Support for more types of graphs, e.g., Heterogeneous graph
- Add Docstrings and Documentation (Building)
- Comprehensive tutorials
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!
Please cite our paper (and the respective papers of the methods used) if you use this code in your own work:
@article{li2021graphgallery,
title={GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software},
author={Li, Jintang and Xu, Kun and Chen, Liang and Zheng, Zibin and Liu, Xiao},
journal={arXiv preprint arXiv:2102.07933},
year={2021}
}