Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions
This work addresses graph classification for machine learning applications, presenting an incremental improvement with fast computation and strong performance.
The authors tackled the problem of graph classification by developing a novel graph kernel framework based on extensions of the Weisfeiler-Lehman isomorphism test, achieving state-of-the-art results on five real-world datasets.
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.