PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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, ride-hail 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.
Original languageEnglish
Title of host publicationCIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages4564–4573
Number of pages10
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - 26 Oct 2021
Event30th ACM International Conference on Information and Knowledge Management - Online, Gold Coast, Australia
Duration: 1 Nov 20215 Nov 2021
https://www.cikm2021.org/

Conference

Conference30th ACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM 2021
Country/TerritoryAustralia
CityGold Coast
Period1/11/215/11/21
Internet address

Keywords

  • neural networks
  • deep learning
  • dynamic graph
  • spatiotemporal data processing

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