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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"

39 / 39 papers shown

Title
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning Alvaro Arroyo Alessio Gravina Benjamin Gutteridge Federico Barbero Claudio Gallicchio Xiaowen Dong Michael M. Bronstein P. Vandergheynst 125 13 0 15 Feb 2025
Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Dai Shi Kuan Yan Lequan Lin Yue Zeng Ting Zhang D. Matsypura Mark C. Gillies Ling Zhu Junbin Gao 171 1 0 10 Feb 2025
Cayley Graph Propagation JJ Wilson Maya Bechler-Speicher Petar Veličković 240 6 0 04 Oct 2024
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature Lukas Fesser Melanie Weber 78 24 0 17 Sep 2023
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 62 12 0 06 Sep 2023
The Expressive Power of Graph Neural Networks: A Survey Bingxue Zhang Changjun Fan Shixuan Liu Kuihua Huang Xiang Zhao Jin-Yu Huang Zhong Liu 208 26 0 16 Aug 2023
How Curvature Enhance the Adaptation Power of Framelet GCNs Dai Shi Yi Guo Zhiqi Shao Junbin Gao 58 14 0 19 Jul 2023
A Survey on Oversmoothing in Graph Neural Networks T. Konstantin Rusch Michael M. Bronstein Siddhartha Mishra 91 213 0 20 Mar 2023
A Generalization of ViT/MLP-Mixer to Graphs Xiaoxin He Bryan Hooi T. Laurent Adam Perold Yann LeCun Xavier Bresson 88 98 0 27 Dec 2022
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks Jhony H. Giraldo Konstantinos Skianis T. Bouwmans Fragkiskos D. Malliaros 63 51 0 05 Dec 2022
Generalized energy and gradient flow via graph framelets Andi Han Dai Shi Zhiqi Shao Junbin Gao 156 13 0 08 Oct 2022
Expander Graph Propagation Andreea Deac Marc Lackenby Petar Velivcković 137 56 0 06 Oct 2022
Oversquashing in GNNs through the lens of information contraction and graph expansion P. Banerjee Kedar Karhadkar Yu Guang Wang Uri Alon Guido Montúfar 56 46 0 06 Aug 2022
DiffWire: Inductive Graph Rewiring via the Lovász Bound Adrián Arnaiz-Rodríguez Ahmed Begga Francisco Escolano Nuria Oliver 83 67 0 15 Jun 2022
Capturing Graphs with Hypo-Elliptic Diffusions Csaba Tóth Darrick Lee Celia Hacker Harald Oberhauser 87 13 0 27 May 2022
Recipe for a General, Powerful, Scalable Graph Transformer Ladislav Rampášek Mikhail Galkin Vijay Prakash Dwivedi Anh Tuan Luu Guy Wolf Dominique Beaini 125 576 0 25 May 2022
Understanding over-squashing and bottlenecks on graphs via curvature Jake Topping Francesco Di Giovanni B. Chamberlain Xiaowen Dong M. Bronstein 108 448 0 29 Nov 2021
Beltrami Flow and Neural Diffusion on Graphs B. Chamberlain J. Rowbottom D. Eynard Francesco Di Giovanni Xiaowen Dong M. Bronstein AI4CE 73 86 0 18 Oct 2021
GRAND: Graph Neural Diffusion B. Chamberlain J. Rowbottom Maria I. Gorinova Stefan Webb Emanuele Rossi M. Bronstein GNN 123 270 0 21 Jun 2021
GraphiT: Encoding Graph Structure in Transformers Grégoire Mialon Dexiong Chen Margot Selosse Julien Mairal 109 170 0 10 Jun 2021
Rethinking Graph Transformers with Spectral Attention Devin Kreuzer Dominique Beaini William L. Hamilton Vincent Létourneau Prudencio Tossou 102 543 0 07 Jun 2021
A Generalization of Transformer Networks to Graphs Vijay Prakash Dwivedi Xavier Bresson AI4CE 105 758 0 17 Dec 2020
Directional Graph Networks Dominique Beaini Saro Passaro Vincent Létourneau William L. Hamilton Gabriele Corso Pietro Lio 90 192 0 06 Oct 2020
TUDataset: A collection of benchmark datasets for learning with graphs Christopher Morris Nils M. Kriege Franka Bause Kristian Kersting Petra Mutzel Marion Neumann 239 827 0 16 Jul 2020
A Note on Over-Smoothing for Graph Neural Networks Chen Cai Yusu Wang 86 276 0 23 Jun 2020
On the Bottleneck of Graph Neural Networks and its Practical Implications Uri Alon Eran Yahav GNN 93 694 0 09 Jun 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta J. Leskovec 309 2,746 0 02 May 2020
A Survey on The Expressive Power of Graph Neural Networks Ryoma Sato 339 173 0 09 Mar 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 458 950 0 02 Mar 2020
Hyperbolic Graph Convolutional Neural Networks Ines Chami Rex Ying Christopher Ré J. Leskovec GNN 113 651 0 28 Oct 2019
Diffusion Improves Graph Learning Johannes Klicpera Stefan Weißenberger Stephan Günnemann GNN 155 711 0 28 Oct 2019
Simplifying Graph Convolutional Networks Felix Wu Tianyi Zhang Amauri Souza Christopher Fifty Tao Yu Kilian Q. Weinberger GNN 246 3,182 0 19 Feb 2019
A Comprehensive Survey on Graph Neural Networks Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang Philip S. Yu FaML GNN AI4TS AI4CE 783 8,566 0 03 Jan 2019
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 257 7,695 0 01 Oct 2018
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 518 1,990 0 09 Jun 2018
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 481 20,233 0 30 Oct 2017
Neural Message Passing for Quantum Chemistry Justin Gilmer S. Schoenholz Patrick F. Riley Oriol Vinyals George E. Dahl 598 7,488 0 04 Apr 2017
Modeling Relational Data with Graph Convolutional Networks Michael Schlichtkrull Thomas Kipf Peter Bloem Rianne van den Berg Ivan Titov Max Welling GNN 194 4,831 0 17 Mar 2017
DeepWalk: Online Learning of Social Representations Bryan Perozzi Rami Al-Rfou Steven Skiena HAI 263 9,800 0 26 Mar 2014