Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Nov 15, 2020
Nov 17, 2020
Jun 28, 2020
Nov 16, 2020
Jun 27, 2020
Jun 27, 2020
Aug 6, 2020
Jun 27, 2020
Nov 17, 2020
Jun 27, 2020
Nov 16, 2020

README.md


PyPI Version Build Status Docs Status Code Coverage

Documentation | External Resources | Datasets

PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric.

The library consists of various dynamic and temporal geometric deep learning, embedding, and spatio-temporal regression methods from a variety of published research papers. In addition, it consists of an easy-to-use dataset loader and iterator for dynamic and temporal graphs, gpu-support. It also comes with a number of benchmark datasets with temporal and dynamic graphs (you can also create your own datasets).


Citing

If you find PyTorch Geometric Temporal and the new datasets useful in your research, please consider adding the following citation:

@misc{pytorch_geometric_temporal,
      author = {Benedek, Rozemberczki and Paul, Scherer},
      title = {{PyTorch Geometric Temporal}},
      year = {2020},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/benedekrozemberczki/pytorch_geometric_temporal}},
}

A simple example

PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy -- see the accompanying tutorial. For example, this is all it takes to implement a recurrent graph convolutional network with two consecutive graph convolutional GRU cells and a linear layer:

import torch
import torch.nn.functional as F
from torch_geometric_temporal.nn.recurrent import GConvGRU

class RecurrentGCN(torch.nn.Module):

    def __init__(self, node_features, num_classes):
        super(RecurrentGCN, self).__init__()
        self.recurrent_1 = GConvGRU(node_features, 32, 5)
        self.recurrent_2 = GConvGRU(32, 16, 5)
        self.linear = torch.nn.Linear(16, num_classes)

    def forward(self, x, edge_index, edge_weight):
        x = self.recurrent_1(x, edge_index, edge_weight)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.recurrent_2(x, edge_index, edge_weight)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.linear(x)
        return F.log_softmax(x, dim=1)

Methods Included

In detail, the following temporal graph neural networks were implemented.

Discrete Recurrent Graph Convolutions

Auxiliary Graph Convolutions


Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets. For a quick start, check out the examples in the examples/ directory.

If you notice anything unexpected, please open an issue. If you are missing a specific method, feel free to open a feature request.


Installation

PyTorch 1.6.0

To install the binaries for PyTorch 1.6.0, simply run

$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal

where ${CUDA} should be replaced by either cpu, cu92, cu101 or cu102 depending on your PyTorch installation.

cpu cu92 cu101 cu102
Linux
Windows
macOS

PyTorch 1.5.0/1.5.1

To install the binaries for PyTorch 1.5.0/1.5.1, simply run

$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.5.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.5.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.5.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.5.0.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal

where ${CUDA} should be replaced by either cpu, cu92, cu101 or cu102 depending on your PyTorch installation.

cpu cu92 cu101 cu102
Linux
Windows
macOS

PyTorch 1.4.0

To install the binaries for PyTorch 1.4.0, simply run

$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal

where ${CUDA} should be replaced by either cpu, cu92, cu100 or cu101 depending on your PyTorch installation.

cpu cu92 cu100 cu101
Linux
Windows
macOS

Running tests

$ python setup.py test

Running examples

$ cd examples
$ python gconvgru_example.py

License

You can’t perform that action at this time.