  We introduce SketchGNN, a convolutional graph neural network for semantic
segmentation and labeling of freehand vector sketches. We treat an input
stroke-based sketch as a graph, with nodes representing the sampled points
along input strokes and edges encoding the stroke structure information. To
predict the per-node labels, our SketchGNN uses graph convolution and a
static-dynamic branching network architecture to extract the features at three
levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN
significantly improves the accuracy of the state-of-the-art methods for
semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in
the component-based metric over a large-scale challenging SPG dataset) and has
magnitudes fewer parameters than both image-based and sequence-based methods.
