302 citations · 324 across the 4 of their papers we have counts for
4 papers
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…
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…
High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline
Laura Antanas, Plinio Moreno, Marion Neumann +4
While grasps must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the…
Propagation Kernels
Marion Neumann, Roman Garnett, Christian Bauckhage +1
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how info…