Recurrent Distance Filtering for Graph Representation Learning

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Recurrent Distance Filtering for Graph Representation Learning

3 December 2023

Antonio Orvieto

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Papers citing "Recurrent Distance Filtering for Graph Representation Learning"

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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 151 13 0 15 Feb 2025
GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Md Atik Ahamed Andrew Cheng Qiang Ye Q. Cheng GNN 114 0 0 01 Feb 2025
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space Xin He Yansen Wang Wenqi Fan Xu Shen Xin Juan Rui Miao Xin Wang 182 2 0 26 Jan 2025
What Can We Learn from State Space Models for Machine Learning on Graphs? Yinan Huang Siqi Miao Pan Li 90 8 0 09 Jun 2024
State Space Model for New-Generation Network Alternative to Transformers: A Survey Tianlin Li Shiao Wang Yuhe Ding Yuehang Li Wentao Wu ... Bowei Jiang Chenglong Li Yaowei Wang Yonghong Tian Jin Tang Mamba 147 53 0 15 Apr 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 218 10 0 27 Dec 2023
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 641 958 0 02 Mar 2020