They find applications in bioinformatics, in chemoinformatics (as a type of molecule kernels[2]), and in social network analysis.
[1] In 2018, Ghosh et al. [7] described the history of graph kernels and their evolution over two decades.
The marginalized graph kernel has been shown to allow accurate predictions of the atomization energy of small organic molecules.
This is equivalent to doing random walks on the direct product of the pair of graphs, and from this, a kernel can be derived that can be efficiently computed.
In those histogram vectors the kernel collects the number of times a color occurs in the graph in every iteration.