Are More Layers Beneficial to Graph Transformers?

1 March 2023

Papers citing "Are More Layers Beneficial to Graph Transformers?"

7 / 7 papers shown

Title
Graph Fourier Transformer with Structure-Frequency Information Yonghui Zhai Yang Zhang Minghao Shang Lihua Pang Yaxin Ren 29 0 0 28 Apr 2025
GRPE: Relative Positional Encoding for Graph Transformer Wonpyo Park Woonggi Chang Donggeon Lee Juntae Kim Seung-won Hwang 39 74 0 30 Jan 2022
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
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 330 0 14 Jun 2018
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
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 208 1,205 0 12 Feb 2018
Benefits of depth in neural networks Matus Telgarsky 121 600 0 14 Feb 2016