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

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

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

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, 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.
Original languageEnglish
Title of host publicationCIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021
EditorsGianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, Hanghang Tong
Number of pages10
PublisherAssociation for Computing Machinery
Publication date2021
Pages4564-4573
DOIs
Publication statusPublished - 2021

Fingerprint

Dive into the research topics of 'PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models'. Together they form a unique fingerprint.

Cite this