Submitted manuscript, 426 KB, PDF document
Submitted manuscript
Research output: Working paper › Preprint
Research output: Working paper › Preprint
}
TY - UNPB
T1 - A Generalized Weisfeiler-Lehman Graph Kernel
AU - Schulz, Till Hendrik
AU - Horváth, Tamás
AU - Welke, Pascal
AU - Wrobel, Stefan
N1 - n/a
PY - 2021/1/20
Y1 - 2021/1/20
N2 - 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.
AB - 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.
KW - cs.LG
M3 - Preprint
BT - A Generalized Weisfeiler-Lehman Graph Kernel
PB - Arxiv
ER -