In this paper we present a novel graph kernel framework inspired the by the Weisfeiler-Lehman (WL) isomorphism tests. Any WL test comprises a relabelling phase of the nodes based on test-specific information extracted from the graph, for example the set of neighbours of a node. We defined a novel relabelling and derived two kernels of the framework from it. The novel kernels are very fast to compute and achieve state-of-the-art results on five real-world datasets.

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions

DA SAN MARTINO, GIOVANNI;NAVARIN, NICOLO';SPERDUTI, ALESSANDRO
2014

Abstract

In this paper we present a novel graph kernel framework inspired the by the Weisfeiler-Lehman (WL) isomorphism tests. Any WL test comprises a relabelling phase of the nodes based on test-specific information extracted from the graph, for example the set of neighbours of a node. We defined a novel relabelling and derived two kernels of the framework from it. The novel kernels are very fast to compute and achieve state-of-the-art results on five real-world datasets.
2014
Neural Information Processing - 21st International Conference, ICONIP
9783319126395
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3156478
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