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A survey on graph kernels

Kriege, Nils M. (author)
Technische Universität Dortmund
Johansson, Fredrik, 1988 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Morris, Christopher (author)
Technische Universität Dortmund
 (creator_code:org_t)
2020-01-14
2020
English.
In: Applied Network Science. - : Springer Science and Business Media LLC. - 2364-8228. ; 5:1
  • Research review (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
NATURVETENSKAP  -- Matematik -- Diskret matematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Discrete Mathematics (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Keyword

Supervised graph classification
Machine learning
Graph kernels

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