A Generalized Weisfeiler-Lehman Graph Kernel

by   Till Hendrik Schulz, et al.

The Weisfeiler-Lehman graph kernels are among the most prevalent graph kernels due to their remarkable time complexity and predictive performance. Their key concept is based on an implicit comparison of neighborhood representing trees with respect to equality (i.e., isomorphism). This binary valued comparison is, however, arguably too rigid for defining suitable similarity measures over graphs. To overcome this limitation, we propose a generalization of Weisfeiler-Lehman graph kernels which takes into account the similarity between trees rather than equality. We achieve this using a specifically fitted variation of the well-known tree edit distance which can efficiently be calculated. We empirically show that our approach significantly outperforms state-of-the-art methods in terms of predictive performance on datasets containing structurally more complex graphs beyond the typically considered molecular graphs.


page 1

page 2

page 3

page 4


Learning from graphs with structural variation

We study the effect of structural variation in graph data on the predict...

Graph Filtration Kernels

The majority of popular graph kernels is based on the concept of Haussle...

Neighborhood Preserving Kernels for Attributed Graphs

We describe the design of a reproducing kernel suitable for attributed g...

Propagation Kernels

We introduce propagation kernels, a general graph-kernel framework for e...

Geometric tree kernels: Classification of COPD from airway tree geometry

Methodological contributions: This paper introduces a family of kernels ...

KONG: Kernels for ordered-neighborhood graphs

We present novel graph kernels for graphs with node and edge labels that...

Literature Review: Graph Kernels in Chemoinformatics

The purpose of this review is to introduce the reader to graph kernels a...

Please sign up or login with your details

Forgot password? Click here to reset