Fetching the paper…
Reading the bibliography…
Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures.
Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
Earlier work this paper cites.
Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search
Taher H Haveliwala · 2003
Earlier work this paper cites.
Link prediction in relational data
Ben Taskar, Ming-Fai Wong, Pieter Abbeel, and Daphne Koller · 2003
Earlier work this paper cites.
The link-prediction problem for social networks
David Liben-Nowell and Jon Kleinberg · 2007
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Data analysis in public social networks
Lubos Takac and Michal Zabovsky · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Earlier work this paper cites.
Censoring representations with an adversary, 2015
Harrison Edwards and Amos Storkey · 2015
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Slr: A scalable latent role model for attribute completion and tie prediction in social networks
Lizi Liao, Qirong Ho, Jing Jiang, and Ee-Peng Lim · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Adversarial attack and defense on graph data: A survey
Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S Yu, Lifang He, and Bo Li · 2018
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Cited alongside, same era.
Compositional fairness constraints for graph embeddings
Avishek Bose and William Hamilton · 2019
Cited alongside, same era.
Censnet: convolution with edge-node switching in graph neural networks
Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
Later among the works it cites.
Graph infomax adversarial learning for treatment effect estimation with networked observational data
Zhixuan Chu, Stephen L Rathbun, and Sheng Li · 2021
Later among the works it cites.
Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Enyan Dai and Suhang Wang · 2021
Later among the works it cites.
Heterogeneous graph neural network via attribute completion
Di Jin, Cuiying Huo, Chundong Liang, and Liang Yang · 2021
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xiaodong Jiang, Pengsheng Ji, and Sheng Li · 2019
Cited alongside, same era.
Fairwalk: Towards fair graph embedding
Tahleen Rahman, Bartlomiej Surma, Michael Backes, and Yang Zhang · 2019
Cited alongside, same era.
Learning on attribute-missing graphs
Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya Zhang, and Ivor W Tsang · 2020
Cited alongside, same era.
Jointly de-biasing face recognition and demographic attribute estimation
Sixue Gong, Xiaoming Liu, and Anil K Jain · 2020
Cited alongside, same era.
Less is more: Data-efficient complex question answering over knowledge bases
Yuncheng Hua, Yuan-Fang Li, Guilin Qi, Wei Wu, Jingyao Zhang, and Daiqing Qi · 2020
Cited alongside, same era.
Co-embedding of nodes and edges with graph neural networks
Xiaodong Jiang, Ronghang Zhu, Pengsheng Ji, and Sheng Li · 2020
Cited alongside, same era.
Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
Cited alongside, same era.
Heng-Shiou Sheu, Zhixuan Chu, Daiqing Qi, and Sheng Li · 2021
Later among the works it cites.
Graph convolutional networks for graphs containing missing features
Hibiki Taguchi, Xin Liu, and Tsuyoshi Murata · 2021
Later among the works it cites.
Transferable feature learning on graphs across visual domains
Ronghang Zhu, Xiaodong Jiang, Jiasen Lu, and Sheng Li · 2021
Later among the works it cites.
Analyzing heterogeneous networks with missing attributes by unsupervised contrastive learning
Dongxiao He, Chundong Liang, Cuiying Huo, Zhiyong Feng, Di Jin, Liang Yang, and Weixiong Zhang · 2022
Later among the works it cites.
Amer: A new attribute-missing network embedding approach
Di Jin, Rui Wang, Tao Wang, Dongxiao He, Weiping Ding, Yuxiao Huang, Longbiao Wang, and Witold Pedrycz · 2022
Later among the works it cites.
Learning fair node representations with graph counterfactual fairness
Jing Ma, Ruocheng Guo, Mengting Wan, Longqi Yang, Aidong Zhang, and Jundong Li · 2022
Later among the works it cites.
Initializing then refining: A simple graph attribute imputation network
Wenxuan Tu, Sihang Zhou, Xinwang Liu, Yue Liu, Zhiping Cai, En Zhu, Changwang Zhang, and Jieren Cheng · 2022
Later among the works it cites.
Unbiased graph embedding with biased graph observations
Nan Wang, Lu Lin, Jundong Li, and Hongning Wang · 2022
Later among the works it cites.
Automated graph learning via population based self-tuning gcn
Ronghang Zhu, Zhiqiang Tao, Yaliang Li, and Sheng Li · 2096
Closest in time.