Weisfeiler and Leman go Machine Learning: The Story so far

18 December 2021

Christopher Morris

Y. Lipman

Haggai Maron

Bastian Alexander Rieck

Nils M. Kriege

Papers citing "Weisfeiler and Leman go Machine Learning: The Story so far"

21 / 21 papers shown

Title
Graph Positional Encoding via Random Feature Propagation Moshe Eliasof Fabrizio Frasca Beatrice Bevilacqua Eran Treister Gal Chechik Haggai Maron 14 16 0 06 Mar 2023
Equivariant Architectures for Learning in Deep Weight Spaces Aviv Navon Aviv Shamsian Idan Achituve Ethan Fetaya Gal Chechik Haggai Maron 19 63 0 30 Jan 2023
On the Ability of Graph Neural Networks to Model Interactions Between Vertices Noam Razin Tom Verbin Nadav Cohen 14 10 0 29 Nov 2022
Beyond 1-WL with Local Ego-Network Encodings Nurudin Alvarez-Gonzalez Andreas Kaltenbrunner Vicencc Gómez 13 5 0 27 Nov 2022
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks Anders Aamand Justin Y. Chen Piotr Indyk Shyam Narayanan R. Rubinfeld Nicholas Schiefer Sandeep Silwal Tal Wagner 30 21 0 06 Nov 2022
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race Franka Bause Nils M. Kriege CLL 8 6 0 19 Sep 2022
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs Yangze Zhou Gitta Kutyniok Bruno Ribeiro OODD AI4CE 73 37 0 30 May 2022
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks Christopher Morris Gaurav Rattan Sandra Kiefer Siamak Ravanbakhsh 23 39 0 25 Mar 2022
Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim Joshua Robinson Lingxiao Zhao Tess E. Smidt S. Sra Haggai Maron Stefanie Jegelka 20 138 0 25 Feb 2022
A Theoretical Comparison of Graph Neural Network Extensions Pál András Papp Roger Wattenhofer 95 45 0 30 Jan 2022
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 120 78 0 01 Oct 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 166 1,095 0 27 Apr 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 44 89 0 25 Feb 2021
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing Jan Toenshoff Martin Ritzert Hinrikus Wolf Martin Grohe GNN 73 27 0 17 Feb 2021
The expressive power of kth-order invariant graph networks Floris Geerts 123 37 0 23 Jul 2020
Graph Homomorphism Convolution Hoang NT Takanori Maehara 58 39 0 03 May 2020
Universal Equivariant Multilayer Perceptrons Siamak Ravanbakhsh 90 48 0 07 Feb 2020
Convolutional Neural Network Architectures for Signals Supported on Graphs Fernando Gama A. Marques G. Leus Alejandro Ribeiro 120 284 0 01 May 2018
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 157 1,748 0 02 Mar 2017
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 219 13,886 0 02 Dec 2016
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,801 0 25 Nov 2016