Spatio-Temporal Graph Scattering Transform


Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theoretical interpretation. To address these issues, we put forth a novel mathematically designed framework to analyze spatio-temporal data. Our proposed spatio-temporal graph scattering transform (ST-GST) extends traditional scattering transforms to the spatiotemporal domain. It performs iterative applications of spatio-temporal graph wavelets and nonlinear activation functions, which can be viewed as a forward pass of spatio-temporal graph convolutional networks without training. Since all the filter coefficients in ST-GST are mathematically designed, it is promising for the real-world scenarios with limited training data, and also allows for a theoretical analysis, which shows that the proposed ST-GST is stable to small perturbations of input signals and structures. Finally, our experiments show that i) ST-GST outperforms spatio-temporal graph convolutional networks by an increase of 35% in accuracy for MSR Action3D dataset; ii) it is better and computationally more efficient to design the transform based on separable spatio-temporal graphs than the joint ones; and iii) the nonlinearity in ST-GST is critical to empirical performance.


  • Related Publication

  •  Pan, C., Chen, S., Ortega, A., "Spatio-Temporal Graph Scattering Transform", arXiv, December 2020.
    BibTeX arXiv
    • @article{Pan2020dec,
    • author = {Pan, Chao and Chen, Siheng and Ortega, Antonio},
    • title = {Spatio-Temporal Graph Scattering Transform},
    • journal = {arXiv},
    • year = 2020,
    • month = dec,
    • url = {}
    • }