research-article

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

Authors:

Benedek Rozemberczki,

George Panagopoulos,

Alexander Riedel,

Maria Astefanoaei,

Guzmán López,

Nicolas Collignon,

Rik SarkarAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4564 - 4573

https://doi.org/10.1145/3459637.3482014

Published: 30 October 2021 Publication History

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.

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Index Terms

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
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Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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https://doi.org/10.1109/TPDS.2025.3532089
Liu TPan SLi P(2025)Streaming Graph Learning in IoT With Storage Optimization and Communication ReductionIEEE Internet of Things Journal10.1109/JIOT.2024.348229212:4(3921-3928)Online publication date: 15-Feb-2025
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