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 neural networks are designed to learn functions on graphs. Typically, the relevant target func...
Random walk kernels have been introduced in seminal work on graph learning and were later largely su...
A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing th...
In this paper we present a novel graph kernel framework inspired the by the Weisfeiler-Lehman (WL) i...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effective...
Linked data arise in many real-world settings - from chemical molecules, and protein-protein interac...
The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and...
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and m...
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and m...
Graph neural networks are designed to learn functions on graphs. Typically, the relevant target func...
Random walk kernels have been introduced in seminal work on graph learning and were later largely su...
A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing th...
In this paper we present a novel graph kernel framework inspired the by the Weisfeiler-Lehman (WL) i...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effective...
Linked data arise in many real-world settings - from chemical molecules, and protein-protein interac...
The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and...
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and m...
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and m...
Graph neural networks are designed to learn functions on graphs. Typically, the relevant target func...
Random walk kernels have been introduced in seminal work on graph learning and were later largely su...
A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing th...