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Residual Network and Embedding Usage: New Tricks of Node Classification
  with Graph Convolutional Networks

Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks

18 May 2021
Huixuan Chi
Yuying Wang
Qinfen Hao
Hong Xia
    GNN
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Papers citing "Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks"

3 / 3 papers shown
Title
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation
  Learning
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning
Siyi Li
Mingrui Zhang
M. Piggott
20
28
0
24 Nov 2022
Scalable deeper graph neural networks for high-performance materials
  property prediction
Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee
Steph-Yves M. Louis
Nihang Fu
Lai Wei
Sourin Dey
Rongzhi Dong
Qinyang Li
Jianjun Hu
70
73
0
25 Sep 2021
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
267
1,944
0
09 Jun 2018
1