Christopher Morris | DeepAI

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∙ 06/06/2023

Fine-grained Expressivity of Graph Neural Networks

Numerous recent works have analyzed the expressive power of message-pass...

0 Jan Böker, et al. ∙

∙ 02/08/2023

Attending to Graph Transformers

Recently, transformer architectures for graphs emerged as an alternative...

0 Luis Müller, et al. ∙

∙ 01/26/2023

WL meet VC

Recently, many works studied the expressive power of graph neural networ...

0 Christopher Morris, et al. ∙

∙ 11/30/2022

Weisfeiler and Leman Go Relational

Knowledge graphs, modeling multi-relational data, improve numerous appli...

0 Pablo Barceló, et al. ∙

∙ 06/22/2022

Ordered Subgraph Aggregation Networks

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged rec...

8 Chendi Qian, et al. ∙

∙ 05/27/2022

MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers

Mixed-integer programming (MIP) technology offers a generic way of formu...

0 Elias B. Khalil, et al. ∙

∙ 03/25/2022

SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks

While (message-passing) graph neural networks have clear limitations in ...

0 Christopher Morris, et al. ∙

∙ 03/04/2022

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Combinatorial optimization is a well-established area in operations rese...

12 Maxime Gasse, et al. ∙

∙ 12/18/2021

Weisfeiler and Leman go Machine Learning: The Story so far

In recent years, algorithms and neural architectures based on the Weisfe...

65 Christopher Morris, et al. ∙

∙ 10/01/2021

Reconstruction for Powerful Graph Representations

Graph neural networks (GNNs) have limited expressive power, failing to r...

12 Leonardo Cotta, et al. ∙

∙ 05/12/2021

The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs

In recent years, algorithms and neural architectures based on the Weisfe...

0 Christopher Morris, et al. ∙

∙ 02/18/2021

Combinatorial optimization and reasoning with graph neural networks

Combinatorial optimization is a well-established area in operations rese...

9 Quentin Cappart, et al. ∙

∙ 07/16/2020

TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning...

25 Christopher Morris, et al. ∙

∙ 01/27/2020

Deep Graph Matching Consensus

This work presents a two-stage neural architecture for learning and refi...

18 Matthias Fey, et al. ∙

∙ 04/02/2019

Towards a practical k-dimensional Weisfeiler-Leman algorithm

The k-dimensional Weisfeiler-Leman algorithm is a well-known heuristic f...

0 Christopher Morris, et al. ∙

∙ 03/28/2019

A Survey on Graph Kernels

Graph kernels have become an established and widely-used technique for s...

0 Nils M. Kriege, et al. ∙

∙ 10/04/2018

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

In recent years, graph neural networks (GNNs) have emerged as a powerful...

0 Christopher Morris, et al. ∙

∙ 06/22/2018

Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of ...

2 Rex Ying, et al. ∙

∙ 06/22/2018

Hierarchical Graph Representation Learning withDifferentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of ...

0 Rex Ying, et al. ∙

∙ 03/07/2017

Global Weisfeiler-Lehman Graph Kernels

Most state-of-the-art graph kernels only take local graph properties int...

0 Christopher Morris, et al. ∙

∙ 03/02/2017

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 ...

0 Nils M. Kriege, et al. ∙

∙ 10/01/2016

Faster Kernels for Graphs with Continuous Attributes via Hashing

While state-of-the-art kernels for graphs with discrete labels scale wel...

0 Christopher Morris, et al. ∙

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