How does over-squashing affect the power of GNNs?

6 June 2023

Papers citing "How does over-squashing affect the power of GNNs?"

9 / 9 papers shown

Title
What makes a good feedforward computational graph? Alex Vitvitskyi J. G. Araújo Marc Lackenby Petar Velickovic 80 1 0 10 Feb 2025
Revisiting Random Walks for Learning on Graphs Jinwoo Kim Olga Zaghen Ayhan Suleymanzade Youngmin Ryou Seunghoon Hong 51 0 0 01 Jul 2024
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond Zhiqi Shao Dai Shi Andi Han Yi Guo Qianchuan Zhao Junbin Gao 11 11 0 06 Sep 2023
Expander Graph Propagation Andreea Deac Marc Lackenby Petar Velivcković 93 51 0 06 Oct 2022
Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch B. Chamberlain Michael W. Mahoney Michael M. Bronstein Siddhartha Mishra 74 53 0 02 Oct 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 163 1,095 0 27 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 178 907 0 02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 226 1,726 0 09 Jun 2018