Paper Info
Title
Graph Invariant Kernels.
Abstract
We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effectively exploit high-dimensional and continuous vertex attributes. Graphs are first decomposed into subgraphs. Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant. By changing this invariant we obtain a family of graph kernels which includes generalizations of Weisfeiler-Lehman, NSPDK, and propagation kernels. We demonstrate empirically that these kernels obtain state-of-the-art results on relational data sets.
Year
Venue
DocType
2015
IJCAI
Conference
Citations 
PageRank 
References 
12
0.84
15
Authors
3
Name
Order
Citations
PageRank
Francesco Orsini1165.82
Paolo Frasconi22984368.70
Luc De Raedt35481505.49