Christopher Morris

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TU Dortmund

Combinatorial optimization and reasoning with graph neural networks

Combinatorial optimization is a well-established area in operations rese...
02/18/2021 ∙ by Quentin Cappart, et al. ∙ 9 ∙ share

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TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning...
07/16/2020 ∙ by Christopher Morris, et al. ∙ 25 ∙ share

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Deep Graph Matching Consensus

This work presents a two-stage neural architecture for learning and refi...
01/27/2020 ∙ by Matthias Fey, et al. ∙ 18 ∙ share

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Towards a practical k-dimensional Weisfeiler-Leman algorithm

The k-dimensional Weisfeiler-Leman algorithm is a well-known heuristic f...
04/02/2019 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

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A Survey on Graph Kernels

Graph kernels have become an established and widely-used technique for s...
03/28/2019 ∙ by Nils M. Kriege, et al. ∙ 0 ∙ share

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Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

In recent years, graph neural networks (GNNs) have emerged as a powerful...
10/04/2018 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

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Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of ...
06/22/2018 ∙ by Rex Ying, et al. ∙ 2 ∙ share

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Hierarchical Graph Representation Learning withDifferentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of ...
06/22/2018 ∙ by Rex Ying, et al. ∙ 0 ∙ share

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Global Weisfeiler-Lehman Graph Kernels

Most state-of-the-art graph kernels only take local graph properties int...
03/07/2017 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

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A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels

Non-linear kernel methods can be approximated by fast linear ones using ...
03/02/2017 ∙ by Nils M. Kriege, et al. ∙ 0 ∙ share

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Faster Kernels for Graphs with Continuous Attributes via Hashing

While state-of-the-art kernels for graphs with discrete labels scale wel...
10/01/2016 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

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Christopher Morris

Featured Co-authors

Jure Leskovec

Xiang Ren

Kristian Kersting

William L. Hamilton

Martin Grohe

Andrea Lodi

Jonathan Masci

Petar Veličković

Petra Mutzel

Rex Ying

Jiaxuan You

Combinatorial optimization and reasoning with graph neural networks

TUDataset: A collection of benchmark datasets for learning with graphs

Deep Graph Matching Consensus

Towards a practical k-dimensional Weisfeiler-Leman algorithm

A Survey on Graph Kernels

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

Hierarchical Graph Representation Learning with Differentiable Pooling

Hierarchical Graph Representation Learning withDifferentiable Pooling

Global Weisfeiler-Lehman Graph Kernels

A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels

Faster Kernels for Graphs with Continuous Attributes via Hashing