  Graph-structured data are an integral part of many application domains,
including chemoinformatics, computational biology, neuroimaging, and social
network analysis. Over the last two decades, numerous graph kernels, i.e.
kernel functions between graphs, have been proposed to solve the problem of
assessing the similarity between graphs, thereby making it possible to perform
predictions in both classification and regression settings. This manuscript
provides a review of existing graph kernels, their applications, software plus
data resources, and an empirical comparison of state-of-the-art graph kernels.
