A survey on graph kernels
Name
41109_2019_Article_195.pdf
Size
2.56 MB
Format
Adobe PDF
Checksum (MD5)
b3778192399562f8adb4100cd9b16967
Author(s)
Kriege, Nils M
Johansson, Fredrik D
Morris, Christopher
Date Issued
January 14, 2020
Publisher
Springer International Publishing
Citation
Applied Network Science. 2020 Jan 14;5(1):6
Version
Final published version
Abstract
Abstract
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s41109-019-0195-3
