  Graph kernels have attracted a lot of attention during the last decade, and
have evolved into a rapidly developing branch of learning on structured data.
During the past 20 years, the considerable research activity that occurred in
the field resulted in the development of dozens of graph kernels, each focusing
on specific structural properties of graphs. Graph kernels have proven
successful in a wide range of domains, ranging from social networks to
bioinformatics. The goal of this survey is to provide a unifying view of the
literature on graph kernels. In particular, we present a comprehensive overview
of a wide range of graph kernels. Furthermore, we perform an experimental
evaluation of several of those kernels on publicly available datasets, and
provide a comparative study. Finally, we discuss key applications of graph
kernels, and outline some challenges that remain to be addressed.
