Oversquashing in GNNs through the lens of information contraction and graph expansion

6 August 2022

Papers citing "Oversquashing in GNNs through the lens of information contraction and graph expansion"

10 / 10 papers shown

Title
When Graph Neural Networks Meet Dynamic Mode Decomposition Dai Shi Lequan Lin Andi Han Zhiyong Wang Yi Guo Junbin Gao AI4CE 23 0 0 08 Oct 2024
Joint Graph Rewiring and Feature Denoising via Spectral Resonance Jonas Linkerhagner Cheng Shi Ivan Dokmanić 33 0 0 13 Aug 2024
Conditional Shift-Robust Conformal Prediction for Graph Neural Network Akansha Agrawal UQCV 45 1 0 20 May 2024
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching Federico Errica Henrik Christiansen Viktor Zaverkin Takashi Maruyama Mathias Niepert Francesco Alesiani 45 6 0 27 Dec 2023
Is Rewiring Actually Helpful in Graph Neural Networks? Domenico Tortorella A. Micheli AI4CE 27 2 0 31 May 2023
Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey L. Delong Ramon Fernández Mir Jacques D. Fleuriot NAI 19 12 0 14 Feb 2023
On the Ability of Graph Neural Networks to Model Interactions Between Vertices Noam Razin Tom Verbin Nadav Cohen 19 10 0 29 Nov 2022
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature K. Nguyen Hieu Nong T. Nguyen Nhat Ho Khuong N. Nguyen Vinh Phu Nguyen 16 61 0 28 Nov 2022
Expander Graph Propagation Andreea Deac Marc Lackenby Petar Velivcković 96 51 0 06 Oct 2022
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 229 1,935 0 09 Jun 2018