README.md

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Documentation | Paper | 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).

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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)

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Methods Included

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

Discrete Recurrent Graph Convolutions

• GConvGRU from Seo et al.: Structured Sequence Modeling with Graph Convolutional Recurrent Networks (ICONIP 2018)

• GConvLSTM from Seo et al.: Structured Sequence Modeling with Graph Convolutional Recurrent Networks (ICONIP 2018)

• GC-LSTM from Chen et al.: GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction (CoRR 2018)

• LRGCN from Li et al.: Predicting Path Failure In Time-Evolving Graphs (KDD 2019)

• DyGrEncoder from Taheri et al.: Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models (WWW 2019)

• EvolveGCNH from Pareja et al.: EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs (AAAI 2020)

• EvolveGCNO from Pareja et al.: EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs (AAAI 2020)

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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.

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Citing

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

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

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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.