Christopher Morris

CoRR, 2024

Probabilistic Graph Rewiring via Virtual Nodes.

[BibT_eX]

[DOI]

CoRR, 2024

Future Directions in Foundations of Graph Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Towards Principled Graph Transformers.

[BibT_eX]

[DOI]

Luis Müller

CoRR, 2024

Towards a Theory of Machine Learning on Graphs and its Applications in Combinatorial Optimization.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Aligning Transformers with Weisfeiler-Leman.

[BibT_eX]

[DOI]

Luis Müller

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Weisfeiler-Leman at the margin: When more expressivity matters.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Future Directions in the Theory of Graph Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Probabilistically Rewired Message-Passing Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems.

[BibT_eX]

[DOI]

Chendi Qian

Didier Chételat

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Combinatorial Optimization and Reasoning with Graph Neural Networks.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2023

Weisfeiler and Leman go Machine Learning: The Story so far.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2023

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

[BibT_eX]

[DOI]

CoRR, 2023

Fine-grained Expressivity of Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

WL meet VC.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132).

[BibT_eX]

[DOI]

Dagstuhl Reports, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Ordered Subgraph Aggregation Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Weisfeiler and Leman Go Relational.

[BibT_eX]

[DOI]

Pablo Barceló

Mikhail Galkin

Miguel A. Romero Orth

Proceedings of the Learning on Graphs Conference, 2022

SpeqNets: Sparsity-aware permutation-equivariant graph networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

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

[BibT_eX]

[DOI]

Elias B. Khalil

Andrea Lodi

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

The Weisfeiler-Leman Method for Machine Learning with Graphs.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning under Resource Constraints - Volume 1: Fundamentals, 2022

2021

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

[BibT_eX]

[DOI]

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Reconstruction for Powerful Graph Representations.

[BibT_eX]

[DOI]

Leonardo Cotta

Bruno Ribeiro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

[BibT_eX]

[DOI]

Matthias Fey

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

TUDataset: A collection of benchmark datasets for learning with graphs.

[BibT_eX]

[DOI]

CoRR, 2020

Classifying Dissemination Processes in Temporal Graphs.

[BibT_eX]

[DOI]

Big Data, 2020

A survey on graph kernels.

[BibT_eX]

[DOI]

Fredrik D. Johansson

Appl. Netw. Sci., 2020

Temporal Graph Kernels for Classifying Dissemination Processes.

[BibT_eX]

[DOI]

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings.

[BibT_eX]

[DOI]

Gaurav Rattan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Graph Matching Consensus.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Lernen mit Graphen: Kern- und neuronale Methoden.

[BibT_eX]

[DOI]

Proceedings of the Ausgezeichnete Informatikdissertationen 2019., 2019

Learning with graphs: kernel and neural approaches.

[BibT_eX]

[DOI]

PhD thesis, 2019

A unifying view of explicit and implicit feature maps of graph kernels.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., 2019

Towards a practical k-dimensional Weisfeiler-Leman algorithm.

[BibT_eX]

[DOI]

CoRR, 2019

Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Hierarchical Graph Representation Learning with Differentiable Pooling.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Property Testing Framework for the Theoretical Expressivity of Graph Kernels.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

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

[BibT_eX]

[DOI]

CoRR, 2017

Global Weisfeiler-Lehman Graph Kernels.

[BibT_eX]

[DOI]

Kristian Kersting

CoRR, 2017

Recent Advances in Kernel-Based Graph Classification.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs.

[BibT_eX]

[DOI]

Kristian Kersting