We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real world problems such as epidemiological forecasting, ridehail demand prediction and web-traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.

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). PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy -- see the accompanying tutorial.