TUDataset: A collection of benchmark datasets for learning with graphs

2 code implementations • 16 Jul 2020 • Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, Marion Neumann

We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.

Graph Classification regression

Propagation kernels: efficient graph kernels from propagated information

1 code implementation • Machine Learning 2019 • Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data.

Graph Classification

An End-to-End Deep Learning Architecture for Graph Classification

2 code implementations • AAAI-18 2018 • Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen

Neural networks are typically designed to deal with data in tensor forms.

General Classification Graph Classification

A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels

no code implementations • 2 Mar 2017 • Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel

On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.

Propagation Kernels

1 code implementation • 13 Oct 2014 • Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data.