mlsanityData to 7 Oct 2026

Space-Time Graph Neural Networks

arXiv 2110.02880 ↗First posted 6 Oct 2021cs.LGcited by 10

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Abstract (arXiv)

We introduce space-time graph neural network (ST-GNN), a novel GNN architecture, tailored to jointly process the underlying space-time topology of time-varying network data. The cornerstone of our proposed architecture is the composition of time and graph convolutional filters followed by pointwise nonlinear activation functions. We introduce a generic definition of convolution operators that mimic the diffusion process of signals over its underlying support. On top of this definition, we propose space-time graph convolutions that are built upon a composition of time and graph shift operators. We prove that ST-GNNs with multivariate integral Lipschitz filters are stable to small perturbations in the underlying graphs as well as small perturbations in the time domain caused by time warping. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs. Numerical experiments with decentralized control systems showcase the effectiveness and stability of the proposed ST-GNNs.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

30 of 46 references.

References · 30

  1. 2017Graph Attention Networkscited by 8,854
  2. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  4. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  7. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  8. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  9. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  10. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  11. 2021GRAND: Graph Neural Diffusioncited by 258
  12. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
18 more
  1. 2019Stability Properties of Graph Neural Networkscited by 125
  2. 2019Continuous Graph Neural Networkscited by 114
  3. 2019Variational Graph Recurrent Neural Networkscited by 86
  4. 2019Graph Neural Networks for Decentralized Multi-Robot Path Planningcited by 84
  5. 2019Learning Decentralized Controllers for Robot Swarms with Graph Neural Networkscited by 62
  6. 2020Gated Graph Recurrent Neural Networkscited by 57
  7. 2018Graph Convolutional Neural Networks via Scatteringcited by 46
  8. 2019Stability of Graph Scattering Transformscited by 34
  9. 2019Recurrent Space-time Graph Neural Networkscited by 19
  10. 2020Spatio-Temporal Graph Scattering Transformcited by 19
  11. 2020Graph and graphon neural network stabilitycited by 15
  12. 2020Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learningcited by 12
  13. 2021BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactionscited by 12
  14. 2021Distributed Linear-Quadratic Control with Graph Neural Networkscited by 12
  15. 2021Graph-Time Convolutional Neural Networkscited by 8
  16. 2021Unsupervised Resource Allocation with Graph Neural Networkscited by 7
  17. 2021Continuous-Depth Neural Models for Dynamic Graph Predictioncited by 7
  18. 2021Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Controlcited by 6

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 1; 2025: 1.

10 papers.

  1. 2023Spatio-Temporal Graph Neural Networks: A Surveycited by 23
  2. 2021Training Stable Graph Neural Networks Through Constrained Learningcited by 12
  3. 2022Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysiscited by 10
  4. 2023Robust Stochastically-Descending Unrolled Networkscited by 9
  5. 2023Stochastic Unrolled Federated Learningcited by 6
  6. 2022Space-Time Graph Neural Networks with Stochastic Graph Perturbationscited by 5
  7. 2022Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysiscited by 3
  8. 2022Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecastingcited by 3
  9. 2025Unrolled Graph Neural Networks for Constrained Optimizationcited by 1
  10. 2024Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?cited by 0