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.