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Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph.
A statistical assessment of subject factors in the pca recognition of human faces
G. Givens, J. R. Beveridge, B. A. Draper, and D. Bolme · 2003
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Face recognition vendor test 2002
P. J. Phillips, P. Grother, R. Micheals, D. M. Blackburn, E. Tabassi, and M. Bone · 2003
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Disclosive ethics and information technology: Disclosing facial recognition systems
L. D. Introna · 2005
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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The case for process fairness in learning: Feature selection for fair decision making
N. Grgic-Hlaca, M. B. Zafar, K. P. Gummadi, and A. Weller · 2016
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node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Cited alongside, same era.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
Cited alongside, same era.
M. J. Kusner, J. R. Loftus, C. Russell, and R. Silva · 2017
Cited alongside, same era.
From parity to preference-based notions of fairness in classification
M. B. Zafar, I. Valera, M. G. Rodriguez, K. P. Gummadi, and A. Weller · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. G. Rogriguez, and K. P. Gummadi · 2017
Cited alongside, same era.
Fairwalk: Towards fair graph embedding
T. Rahman, B. Surma, M. Backes, and Y. Zhang · 2019
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On dyadic fairness: Exploring and mitigating bias in graph connections
P. Li, Y. Wang, H. Zhao, P. Hong, and H. Liu · 2020
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Towards a unified framework for fair and stable graph representation learning
C. Agarwal, H. Lakkaraju, and M. Zitnik · 2021
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Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
E. Dai and S. Wang · 2021
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A survey on bias and fairness in machine learning
N. ”Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan · 2021
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Biased edge dropout for enhancing fairness in graph representation learning
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J. Klicpera, A. Bojchevski, and S. Günnemann · 2018
Cited alongside, same era.
Adversarial attacks on neural networks for graph data
D. Zügner, A. Akbarnejad, and S. Günnemann · 2018
Cited alongside, same era.
I. Spinelli, S. Scardapane, A. Hussain, and A. Uncini · 2021
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