Understanding Oversquashing in GNNs through the Lens of Effective
Resistance

Understanding Oversquashing in GNNs through the Lens of Effective Resistance

14 February 2023

Papers citing "Understanding Oversquashing in GNNs through the Lens of Effective Resistance"

17 / 17 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 52 6 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 70 1 0 10 Feb 2025
All You Need is Resistance: On the Equivalence of Effective Resistance and Certain Optimal Transport Problems on Graphs Sawyer Robertson Zhengchao Wan Alexander Cloninger OT 71 2 0 28 Jan 2025
Cayley Graph Propagation JJ Wilson Maya Bechler-Speicher Petar Veličković 58 6 0 04 Oct 2024
Joint Graph Rewiring and Feature Denoising via Spectral Resonance Jonas Linkerhagner Cheng Shi Ivan Dokmanić 59 0 0 13 Aug 2024
Revisiting Random Walks for Learning on Graphs Jinwoo Kim Olga Zaghen Ayhan Suleymanzade Youngmin Ryou Seunghoon Hong 79 1 0 01 Jul 2024
Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering Mitchell Black Lucy Lin A. Nayyeri Weng-Keen Wong 85 0 0 04 Jun 2024
Bundle Neural Networks for message diffusion on graphs Jacob Bamberger Federico Barbero Xiaowen Dong Michael M. Bronstein 64 2 0 24 May 2024
Conditional Shift-Robust Conformal Prediction for Graph Neural Network Akansha Agrawal UQCV 72 1 0 20 May 2024
Expander Graph Propagation Andreea Deac Marc Lackenby Petar Velivcković 104 55 0 06 Oct 2022
DiffWire: Inductive Graph Rewiring via the Lovász Bound Adrián Arnaiz-Rodríguez Ahmed Begga Francisco Escolano Nuria Oliver 37 63 0 15 Jun 2022
Understanding over-squashing and bottlenecks on graphs via curvature Jake Topping Francesco Di Giovanni B. Chamberlain Xiaowen Dong M. Bronstein 71 437 0 29 Nov 2021
TUDataset: A collection of benchmark datasets for learning with graphs Christopher Morris Nils M. Kriege Franka Bause Kristian Kersting Petra Mutzel Marion Neumann 73 802 0 16 Jul 2020
A Note on Over-Smoothing for Graph Neural Networks Chen Cai Yusu Wang 40 266 0 23 Jun 2020
On the Bottleneck of Graph Neural Networks and its Practical Implications Uri Alon Eran Yahav GNN 65 675 0 09 Jun 2020
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 62 7,554 0 01 Oct 2018
Relational inductive biases, deep learning, and graph networks Peter W. Battaglia Jessica B. Hamrick V. Bapst Alvaro Sanchez-Gonzalez V. Zambaldi ... Pushmeet Kohli M. Botvinick Oriol Vinyals Yujia Li Razvan Pascanu AI4CE NAI 161 3,101 0 04 Jun 2018

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