rusty1s/pytorch_geometric
Geometric Deep Learning Extension Library for PyTorch
repo name  rusty1s/pytorch_geometric 
repo link  https://github.com/rusty1s/pytorch_geometric 
homepage  https://pytorchgeometric.readthedocs.io/ 
language  Python 
size (curr.)  2633 kB 
stars (curr.)  6797 
created  20171006 
license  MIT License 
Documentation  Paper  External Resources
PyTorch Geometric (PyG) is a geometric deep learning extension library for PyTorch.
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easytouse minibatch loader for many small and single giant graphs, multi gpusupport, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
PyTorch Geometric makes implementing Graph Neural Networks a breeze (see here for the accompanying tutorial). For example, this is all it takes to implement the edge convolutional layer:
import torch
from torch.nn import Sequential as Seq, Linear as Lin, ReLU
from torch_geometric.nn import MessagePassing
class EdgeConv(MessagePassing):
def __init__(self, F_in, F_out):
super(EdgeConv, self).__init__(aggr='max') # "Max" aggregation.
self.mlp = Seq(Lin(2 * F_in, F_out), ReLU(), Lin(F_out, F_out))
def forward(self, x, edge_index):
# x has shape [N, F_in]
# edge_index has shape [2, E]
return self.propagate(edge_index, x=x) # shape [N, F_out]
def message(self, x_i, x_j):
# x_i has shape [E, F_in]
# x_j has shape [E, F_in]
edge_features = torch.cat([x_i, x_j  x_i], dim=1) # shape [E, 2 * F_in]
return self.mlp(edge_features) # shape [E, F_out]
In detail, the following methods are currently implemented:
 SplineConv from Fey et al.: SplineCNN: Fast Geometric Deep Learning with Continuous BSpline Kernels (CVPR 2018)
 GCNConv from Kipf and Welling: SemiSupervised Classification with Graph Convolutional Networks (ICLR 2017)
 ChebConv from Defferrard et al.: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (NIPS 2016)
 NNConv from Gilmer et al.: Neural Message Passing for Quantum Chemistry (ICML 2017)
 CGConv from Xie and Grossman: Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties (Physical Review Letters 120, 2018)
 ECConv from Simonovsky and Komodakis: EdgeConditioned Convolution on Graphs (CVPR 2017)
 GATConv from Veličković et al.: Graph Attention Networks (ICLR 2018)
 SAGEConv from Hamilton et al.: Inductive Representation Learning on Large Graphs (NIPS 2017)
 GraphConv from, e.g., Morris et al.: Weisfeiler and Leman Go Neural: Higherorder Graph Neural Networks (AAAI 2019)
 GatedGraphConv from Li et al.: Gated Graph Sequence Neural Networks (ICLR 2016)
 GINConv from Xu et al.: How Powerful are Graph Neural Networks? (ICLR 2019)
 ARMAConv from Bianchi et al.: Graph Neural Networks with Convolutional ARMA Filters (CoRR 2019)
 SGConv from Wu et al.: Simplifying Graph Convolutional Networks (CoRR 2019)
 APPNP from Klicpera et al.: Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019)
 AGNNConv from Thekumparampil et al.: Attentionbased Graph Neural Network for SemiSupervised Learning (CoRR 2017)
 TAGConv from Du et al.: Topology Adaptive Graph Convolutional Networks (CoRR 2017)
 RGCNConv from Schlichtkrull et al.: Modeling Relational Data with Graph Convolutional Networks (ESWC 2018)
 SignedConv from Derr et al.: Signed Graph Convolutional Network (ICDM 2018)
 DNAConv from Fey: Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks (ICLRW 2019)
 EdgeConv from Wang et al.: Dynamic Graph CNN for Learning on Point Clouds (CoRR, 2018)
 PointConv (including Iterative Farthest Point Sampling, dynamic graph generation based on nearest neighbor or maximum distance, and kNN interpolation for upsampling) from Qi et al.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (CVPR 2017) and PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (NIPS 2017)
 XConv from Li et al.: PointCNN: Convolution On XTransformed Points (official implementation) (NeurIPS 2018)
 PPFConv from Deng et al.: PPFNet: Global Context Aware Local Features for Robust 3D Point Matching (CVPR 2018)
 GMMConv from Monti et al.: Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs (CVPR 2017)
 FeaStConv from Verma et al.: FeaStNet: FeatureSteered Graph Convolutions for 3D Shape Analysis (CVPR 2018)
 HypergraphConv from Bai et al.: Hypergraph Convolution and Hypergraph Attention (CoRR 2019)
 A MetaLayer for building any kind of graph network similar to the TensorFlow Graph Nets library from Battaglia et al.: Relational Inductive Biases, Deep Learning, and Graph Networks (CoRR 2018)
 GlobalAttention from Li et al.: Gated Graph Sequence Neural Networks (ICLR 2016)
 Set2Set from Vinyals et al.: Order Matters: Sequence to Sequence for Sets (ICLR 2016)
 Sort Pool from Zhang et al.: An EndtoEnd Deep Learning Architecture for Graph Classification (AAAI 2018)
 Dense Differentiable Pooling from Ying et al.: Hierarchical Graph Representation Learning with Differentiable Pooling (NeurIPS 2018)
 Dense MinCUT Pooling from Bianchi et al.: MinCUT Pooling in Graph Neural Networks (CoRR 2019)
 Graclus Pooling from Dhillon et al.: Weighted Graph Cuts without Eigenvectors: A Multilevel Approach (PAMI 2007)
 Voxel Grid Pooling from, e.g., Simonovsky and Komodakis: Dynamic EdgeConditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)
 TopK Pooling from Gao and Ji: Graph UNets (ICML 2019), Cangea et al.: Towards Sparse Hierarchical Graph Classifiers (NeurIPSW 2018) and Knyazev et al.: Understanding Attention and Generalization in Graph Neural Networks (ICLRW 2019)
 SAG Pooling from Lee et al.: SelfAttention Graph Pooling (ICML 2019) and Knyazev et al.: Understanding Attention and Generalization in Graph Neural Networks (ICLRW 2019)
 Edge Pooling from Diehl et al.: Towards Graph Pooling by Edge Contraction (ICMLW 2019) and Diehl: Edge Contraction Pooling for Graph Neural Networks (CoRR 2019)
 Local Degree Profile from Cai and Wang: A Simple yet Effective Baseline for Nonattribute Graph Classification (CoRR 2018)
 Jumping Knowledge from Xu et al.: Representation Learning on Graphs with Jumping Knowledge Networks (ICML 2018)
 Node2Vec from Grover and Leskovec: node2vec: Scalable Feature Learning for Networks (KDD 2016)
 Deep Graph Infomax from Veličković et al.: Deep Graph Infomax (ICLR 2019)
 All variants of Graph AutoEncoders from Kipf and Welling: Variational Graph AutoEncoders (NIPSW 2016) and Pan et al.: Adversarially Regularized Graph Autoencoder for Graph Embedding (IJCAI 2018)
 RENet from Jin et al.: Recurrent Event Network for Reasoning over Temporal Knowledge Graphs (ICLRW 2019)
 GraphUNet from Gao and Ji: Graph UNets (ICML 2019)
 NeighborSampler from Hamilton et al.: Inductive Representation Learning on Large Graphs (NIPS 2017)
 ClusterGCN from Chiang et al.: ClusterGCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (KDD 2019)
 GraphSAINT from Zeng et al.: GraphSAINT: Graph Sampling Based Inductive Learning Method (ICLR 2020)
 GDC from Klicpera et al.: Diffusion Improves Graph Learning (NeurIPS 2019)
 GraphSizeNorm from Dwivedi et al.: Benchmarking Graph Neural Networks (CoRR 2020)
 GNNExplainer from Ying et al.: GNNExplainer: Generating Explanations for Graph Neural Networks (NeurIPS 2019)
Head over to our documentation to find out more about installation, data handling, creation of datasets and a full list of implemented methods, transforms, and datasets.
For a quick start, check out our examples in the examples/
directory.
If you notice anything unexpected, please open an issue and let us know. If you are missing a specific method, feel free to open a feature request. We are motivated to constantly make PyTorch Geometric even better.
Installation
We provide pip wheels for all major OS/PyTorch/CUDA combinations, see here.
cpu 
cu92 
cu100 
cu101 


Linux  ✅  ✅  ✅  ✅ 
Windows  ✅  ❌  ❌  ✅ 
macOS  ✅ 
To install the binaries, first ensure that PyTorch 1.4.0 is installed, e.g.:
$ python c "import torch; print(torch.__version__)"
>>> 1.4.0
Then run
$ pip install torchscatter==latest+${CUDA} f https://pytorchgeometric.com/whl/torch1.4.0.html
$ pip install torchsparse==latest+${CUDA} f https://pytorchgeometric.com/whl/torch1.4.0.html
$ pip install torchcluster==latest+${CUDA} f https://pytorchgeometric.com/whl/torch1.4.0.html
$ pip install torchsplineconv==latest+${CUDA} f https://pytorchgeometric.com/whl/torch1.4.0.html
$ pip install torchgeometric
where ${CUDA}
should be replaced by either cpu
, cu92
, cu100
or cu101
depending on your PyTorch installation.
Running examples
$ cd examples
$ python gcn.py
Cite
Please cite our paper (and the respective papers of the methods used) if you use this code in your own work:
@inproceedings{Fey/Lenssen/2019,
title={Fast Graph Representation Learning with {PyTorch Geometric}},
author={Fey, Matthias and Lenssen, Jan E.},
booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
year={2019},
}
Feel free to email us if you wish your work to be listed in the external resources.
Running tests
$ python setup.py test