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Graph Invariant Kernels
lib:48e9ea7b48a08578 (v1.0.0)
Authors: Francesco Orsini,Paolo Frasconi,Luc De Raedt
Where published: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) 2015 7
Document: PDF DOI
Abstract URL: https://dl.acm.org/citation.cfm?id=2832747.2832773
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.
Where published: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) 2015 7
Document: PDF DOI
Abstract URL: https://dl.acm.org/citation.cfm?id=2832747.2832773
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.
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