  This paper follows cognitive studies to investigate a graph representation
for sketches, where the information of strokes, i.e., parts of a sketch, are
encoded on vertices and information of inter-stroke on edges. The resultant
graph representation facilitates the training of a Graph Neural Networks for
classification tasks, and achieves accuracy and robustness comparable to the
state-of-the-art against translation and rotation attacks, as well as stronger
attacks on graph vertices and topologies, i.e., modifications and addition of
strokes, all without resorting to adversarial training. Prior studies on
sketches, e.g., graph transformers, encode control points of stroke on
vertices, which are not invariant to spatial transformations. In contrary, we
encode vertices and edges using pairwise distances among control points to
achieve invariance. Compared with existing generative sketch model for one-shot
classification, our method does not rely on run-time statistical inference.
Lastly, the proposed representation enables generation of novel sketches that
are structurally similar to while separable from the existing dataset.
