  We propose an extension of Convolutional Neural Networks (CNNs) to
graph-structured data, including strided convolutions and data augmentation on
graphs.
  Our method matches the accuracy of state-of-the-art CNNs when applied on
images, without any prior about their 2D regular structure.
  On fMRI data, we obtain a significant gain in accuracy compared with existing
graph-based alternatives.
