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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.

The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. Take a look at this introductory example of using PyTorch Geometric Temporal with Pytorch Lighning.

We also provide detailed examples for each of the recurrent models and notebooks for the attention based ones.

Case Study Tutorials

We provide in-depth case study tutorials in the Documentation, each covers an aspect of PyTorch Geometric Temporal’s functionality.

Incremental TrainingEpidemiological Forecasting Case Study

Cumulative TrainingWeb Traffic Management Case Study


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

               author = {Benedek Rozemberczki and Paul Scherer and Yixuan He and George Panagopoulos and Alexander Riedel and Maria Astefanoaei and Oliver Kiss and Ferenc Beres and Guzman Lopez and Nicolas Collignon and Rik Sarkar},
               title = {{PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models}},
               year = {2021},
               booktitle={Proceedings of the 30th ACM International Conference on Information and Knowledge Management},
               pages = {4564–4573},

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,
        x = self.recurrent_2(x, edge_index, edge_weight)
        x = F.relu(x)
        x = F.dropout(x,
        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

Attention Aggregated 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.


First install pytorch and pytorch-geometric and then run

pip install torch-geometric-temporal

Running tests

$ python -m pytest test