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Promoting Fairness in GNNs: A Characterization of Stability
v1v2v3 (latest)

Promoting Fairness in GNNs: A Characterization of Stability

7 September 2023
Yaning Jia
Chunhui Zhang
    AAML
ArXiv (abs)PDFHTML

Papers citing "Promoting Fairness in GNNs: A Characterization of Stability"

17 / 17 papers shown
Title
Are Defenses for Graph Neural Networks Robust?
Are Defenses for Graph Neural Networks Robust?
Felix Mujkanovic
Simon Geisler
Stephan Günnemann
Aleksandar Bojchevski
OODAAML
70
59
0
31 Jan 2023
Characterizing the Influence of Graph Elements
Characterizing the Influence of Graph Elements
Zizhang Chen
Peizhao Li
Hongfu Liu
Pengyu Hong
TDI
34
22
0
14 Oct 2022
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics
  for Session-based Recommendation
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
Chao Huang
Jiahui Chen
Lianghao Xia
Yong-mei Xu
Peng Dai
Yanqing Chen
Liefeng Bo
Jiashu Zhao
Xiangji Huang
161
98
0
08 Oct 2021
Lipschitz Normalization for Self-Attention Layers with Application to
  Graph Neural Networks
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
George Dasoulas
Kevin Scaman
Aladin Virmaux
GNN
63
39
0
08 Mar 2021
On Explainability of Graph Neural Networks via Subgraph Explorations
On Explainability of Graph Neural Networks via Subgraph Explorations
Hao Yuan
Haiyang Yu
Jie Wang
Kang Li
Shuiwang Ji
FAtt
83
390
0
09 Feb 2021
The Lipschitz Constant of Self-Attention
The Lipschitz Constant of Self-Attention
Hyunjik Kim
George Papamakarios
A. Mnih
74
145
0
08 Jun 2020
DeBayes: a Bayesian Method for Debiasing Network Embeddings
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl
T. D. Bie
FaMLCML
53
78
0
26 Feb 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural
  Networks
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Q. Huang
M. Yamada
Yuan Tian
Dinesh Singh
Dawei Yin
Yi-Ju Chang
FAtt
90
357
0
17 Jan 2020
Operationalizing Individual Fairness with Pairwise Fair Representations
Operationalizing Individual Fairness with Pairwise Fair Representations
Preethi Lahoti
Krishna P. Gummadi
Gerhard Weikum
FaML
80
101
0
02 Jul 2019
Compositional Fairness Constraints for Graph Embeddings
Compositional Fairness Constraints for Graph Embeddings
A. Bose
William L. Hamilton
FaML
70
259
0
25 May 2019
GNNExplainer: Generating Explanations for Graph Neural Networks
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying
Dylan Bourgeois
Jiaxuan You
Marinka Zitnik
J. Leskovec
LLMAG
150
1,323
0
10 Mar 2019
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
243
7,653
0
01 Oct 2018
Link Prediction Based on Graph Neural Networks
Link Prediction Based on Graph Neural Networks
Muhan Zhang
Yixin Chen
GNN
100
1,937
0
27 Feb 2018
Inductive Representation Learning on Large Graphs
Inductive Representation Learning on Large Graphs
William L. Hamilton
Z. Ying
J. Leskovec
509
15,247
0
07 Jun 2017
Variational Graph Auto-Encoders
Variational Graph Auto-Encoders
Thomas Kipf
Max Welling
GNNBDLSSLCML
151
3,586
0
21 Nov 2016
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
228
4,312
0
07 Oct 2016
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
644
29,076
0
09 Sep 2016
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