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Theoretical and Empirical Insights into the Origins of Degree Bias in
  Graph Neural Networks

Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks

4 April 2024
Arjun Subramonian
Jian Kang
Yizhou Sun
    AI4CE
ArXivPDFHTML

Papers citing "Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks"

5 / 5 papers shown
Title
Subgraph Federated Learning for Local Generalization
Sungwon Kim
Yoonho Lee
Yunhak Oh
Namkyeong Lee
Sukwon Yun
Junseok Lee
Sein Kim
Carl Yang
Chanyoung Park
FedML
OOD
79
1
0
06 Mar 2025
Uncovering the Structural Fairness in Graph Contrastive Learning
Uncovering the Structural Fairness in Graph Contrastive Learning
Ruijia Wang
Xiao Wang
Chuan Shi
Le Song
58
31
0
06 Oct 2022
Multi-scale Attributed Node Embedding
Multi-scale Attributed Node Embedding
Benedek Rozemberczki
Carl Allen
Rik Sarkar
GNN
145
828
0
28 Sep 2019
Contextual Stochastic Block Models
Contextual Stochastic Block Models
Y. Deshpande
Andrea Montanari
Elchanan Mossel
S. Sen
100
151
0
23 Jul 2018
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
229
1,935
0
09 Jun 2018
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