Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

6 April 2020

Papers citing "Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework"

8 / 8 papers shown

Title
On the Ability of Graph Neural Networks to Model Interactions Between Vertices Noam Razin Tom Verbin Nadav Cohen 19 10 0 29 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 32 21 0 06 Nov 2022
Theory of Graph Neural Networks: Representation and Learning Stefanie Jegelka GNN AI4CE 24 67 0 16 Apr 2022
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks Christopher Morris Gaurav Rattan Sandra Kiefer Siamak Ravanbakhsh 33 39 0 25 Mar 2022
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 122 78 0 01 Oct 2021
Geometric Deep Learning on Molecular Representations Kenneth Atz F. Grisoni G. Schneider AI4CE 22 285 0 26 Jul 2021
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 12 6 0 13 May 2020
A Survey on The Expressive Power of Graph Neural Networks Ryoma Sato 170 170 0 09 Mar 2020