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Github GitHub - benedekrozemberczki/pytorch_geometric_temporal: A Temporal Exten...

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source link: https://github.com/benedekrozemberczki/pytorch_geometric_temporal
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README.md



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. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemological forecasting, sharing economy, energy production and web traffic management domains. Finally, 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 and Yixuan, He and George, Panagopoulos and Maria, Astefanoaei and Oliver, Kiss and Ferenc, Beres and Nicolas, Collignon},
      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.

Recurrent Graph Convolutions

Temporal 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 1.8.0

To install the binaries for PyTorch 1.8.0, simply run

$ pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal

where ${CUDA} should be replaced by either cpu, cu101, cu102, or cu111 depending on your PyTorch installation.

cpu cu101 cu102 cu111 Linux white_check_markwhite_check_markwhite_check_markwhite_check_markWindows white_check_markwhite_check_markwhite_check_markwhite_check_markmacOS white_check_mark

PyTorch 1.7.0

To install the binaries for PyTorch 1.7.0, simply run

$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal

where ${CUDA} should be replaced by either cpu, cu92, cu101, cu102 or cu110 depending on your PyTorch installation.

cpu cu92 cu101 cu102 cu110 Linux white_check_markwhite_check_markwhite_check_markwhite_check_markwhite_check_markWindows white_check_markxwhite_check_markwhite_check_markwhite_check_markmacOS white_check_mark

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 white_check_markwhite_check_markwhite_check_markwhite_check_markWindows white_check_markxwhite_check_markwhite_check_markmacOS white_check_mark

Running tests

$ python setup.py test

License


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