# Graph Kernels: A Survey

## TL;DR

This survey reviews the development, types, and applications of graph kernels over the past two decades, highlighting their success across various domains and providing a comparative evaluation of several kernels.

## Contribution

It offers a comprehensive overview of graph kernels, compares multiple methods experimentally, and discusses future challenges in the field.

## Key findings

- Different graph kernels capture various structural properties.
- Experimental results show varying performance across datasets.
- Graph kernels are effective in social network and bioinformatics applications.

## Abstract

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.