Exposition on over-squashing problem on GNNs: Current Methods,
Benchmarks and Challenges

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges

13 November 2023

Dai Shi

Junbin Gao

Papers citing "Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges"

9 / 9 papers shown

Title
When Graph Neural Networks Meet Dynamic Mode Decomposition Dai Shi Lequan Lin Andi Han Zhiyong Wang Yi Guo Junbin Gao AI4CE 11 0 0 08 Oct 2024
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature Lukas Fesser Melanie Weber 54 8 0 17 Sep 2023
Frameless Graph Knowledge Distillation Dai Shi Zhiqi Shao Yi Guo Junbin Gao 8 4 0 13 Jul 2023
Generalized energy and gradient flow via graph framelets Andi Han Dai Shi Zhiqi Shao Junbin Gao 59 12 0 08 Oct 2022
Expander Graph Propagation Andreea Deac Marc Lackenby Petar Velivcković 90 41 0 06 Oct 2022
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 156 463 0 31 Dec 2020
A Survey on The Expressive Power of Graph Neural Networks Ryoma Sato 159 170 0 09 Mar 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 173 907 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 217 1,726 0 09 Jun 2018