Graph Kernels: A Survey
It synthesizes existing research on graph kernels for researchers in machine learning and structured data domains, but is incremental as it reviews and compares prior work.
This survey provides a unifying overview of graph kernels, which have evolved over the past two decades into a key method for learning on structured data, and includes an experimental evaluation comparing several kernels on public datasets.
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.