302 citations · 573 across the 28 of their papers we have counts for
5 papers · 1 filter
TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause +3
Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark d…
Global Weisfeiler-Lehman Graph Kernels
Christopher Morris, Kristian Kersting, Petra Mutzel
Most state-of-the-art graph kernels only take local graph properties into account, i.e., the kernel is computed with regard to properties of the neighborhood of vertices or other s…
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
Nils M. Kriege, Marion Neumann, Christopher Morris +2
Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convol…
Faster Kernels for Graphs with Continuous Attributes via Hashing
Christopher Morris, Nils M. Kriege, Kristian Kersting +1
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortu…
Subgraph Matching Kernels for Attributed Graphs
Nils Kriege, Petra Mutzel
We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al.…