  We study the effect of structural variation in graph data on the predictive
performance of graph kernels. To this end, we introduce a novel, noise-robust
adaptation of the GraphHopper kernel and validate it on benchmark data,
obtaining modestly improved predictive performance on a range of datasets.
Next, we investigate the performance of the state-of-the-art Weisfeiler-Lehman
graph kernel under increasing synthetic structural errors and find that the
effect of introducing errors depends strongly on the dataset.
