1-WL Expressiveness Is (Almost) All You Need

21 February 2022

Papers citing "1-WL Expressiveness Is (Almost) All You Need"

6 / 6 papers shown

Title
A Generalization of ViT/MLP-Mixer to Graphs Xiaoxin He Bryan Hooi T. Laurent Adam Perold Yann LeCun Xavier Bresson 22 88 0 27 Dec 2022
Pure Transformers are Powerful Graph Learners Jinwoo Kim Tien Dat Nguyen Seonwoo Min Sungjun Cho Moontae Lee Honglak Lee Seunghoon Hong 19 187 0 06 Jul 2022
Evaluating Self-Supervised Learning for Molecular Graph Embeddings Hanchen Wang Jean Kaddour Shengchao Liu Jian Tang Joan Lasenby Qi Liu 17 20 0 16 Jun 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 172 1,100 0 27 Apr 2021
A Survey on The Expressive Power of Graph Neural Networks Ryoma Sato 167 170 0 09 Mar 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 183 913 0 02 Mar 2020