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Graph Neural Networks and Time Series as Directed Graphs for Quality Recognition

arXiv 2310.02774 ↗First posted 4 Oct 2023cs.LGcited by 0

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Abstract (arXiv)

Graph Neural Networks (GNNs) are becoming central in the study of time series, coupled with existing algorithms as Temporal Convolutional Networks and Recurrent Neural Networks. In this paper, we see time series themselves as directed graphs, so that their topology encodes time dependencies and we start to explore the effectiveness of GNNs architectures on them. We develop two distinct Geometric Deep Learning models, a supervised classifier and an autoencoder-like model for signal reconstruction. We apply these models on a quality recognition problem.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 4 Oct 2023.

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12 of 22 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  7. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  8. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  9. 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 214
  10. 20191D Convolutional Neural Networks and Applications: A Surveycited by 153
  11. 2020Directed Graph Convolutional Networkcited by 73
  12. 2023Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicistscited by 3

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