Graph Kernels: State-of-the-Art and Future Challenges

TL;DR

This paper reviews the development, applications, and empirical performance of various graph kernels, highlighting current state-of-the-art methods and future challenges in graph similarity assessment.

Contribution

It provides a comprehensive survey of existing graph kernels, compares their empirical performance, and discusses future research directions in the field.

Findings

Empirical comparison of state-of-the-art graph kernels

Identification of strengths and weaknesses of different kernels

Discussion of software and data resources for graph kernels

Abstract

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.