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Graph mining algorithms have been playing a significant role in myriad fields over the years.
Measurement of diversity
Edward H Simpson · 1949
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Perception of self, generalized stereotypes, and brand selection
Edward L Grubb and Gregg Hupp · 1968
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The anatomy of a large-scale hypertextual web search engine
Sergey Brin and Lawrence Page · 1998
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On a technique for measurement of turbulent shear stress in the presence of surface waves
JH Trowbridge · 1998
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A theory of justice: Revised edition
John Rawls · 1999
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Cumulated gain-based evaluation of ir techniques
Kalervo J. and Jaana K · 2002
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Link prediction using supervised learning
Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki · 2006
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Modularity and community structure in networks
Mark EJ Newman · 2006
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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The dynamics of viral marketing
Jure Leskovec, Lada A Adamic, and Bernardo A Huberman · 2007
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The link-prediction problem for social networks
David Liben-Nowell and Jon Kleinberg · 2007
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Arnetminer: extraction and mining of academic social networks
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su · 2008
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Group recommendation: Semantics and efficiency
Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawlat, Gautam Das, and Cong Yu · 2009
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Expected reciprocal rank for graded relevance
Olivier Chapelle, Donald Metlzer, Ya Zhang, and Pierre Grinspan · 2009
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Efficient influence maximization in social networks
Wei Chen, Yajun Wang, and Siyu Yang · 2009
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Causality
Judea Pearl · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011)
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik · 2011
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Link prediction in complex networks: A survey
Linyuan Lü and Tao Zhou · 2011
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Comparing community structure to characteristics in online collegiate social networks
Veronica Red, Eric D Kelsic, Peter J Mucha, and Mason A Porter · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Learning to discover social circles in ego networks
Julian J McAuley and Jure Leskovec · 2012
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The filter bubble: What the internet is hiding
Eli Pariser · 2012
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Mining heterogeneous information networks: principles and methodologies
Yizhou Sun and Jiawei Han · 2012
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Data analysis in public social networks
Lubos Takac and Michal Zabovsky · 2012
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mtrust: Discerning multi-faceted trust in a connected world
Jiliang Tang, Huiji Gao, and Huan Liu · 2012
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The diffusion of microfinance
Abhijit Banerjee, Arun G Chandrasekhar, Esther Duflo, and Matthew O Jackson · 2013
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A novel bayesian similarity measure for recommender systems
Guibing Guo, Jie Zhang, and Neil Yorke-Smith · 2013
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Efficiency improvement of neutrality-enhanced recommendation
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2013
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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The effect of race/ethnicity on sentencing: Examining sentence type, jail length, and prison length
Kareem L Jordan and Tina L Freiburger · 2015
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Jenna Burrell · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley · 2016
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Incorporating diversity in a learning to rank recommender system
Jacek Wasilewski and Neil Hurley · 2016
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Controlling popularity bias in learning-to-rank recommendation
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher · 2017
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Balanced neighborhoods for fairness-aware collaborative recommendation
Robin Burke, Nasim Sonboli, Masoud Mansoury, and Aldo Ordoñez-Gauger · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Considerations on recommendation independence for a find-good-items task
Toshihiro Kamishima and Shotaro Akaho · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Fairness-aware group recommendation with pareto-efficiency
Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, and Shaoping Ma · 2017
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The authority of “fair” in machine learning
Michael Skirpan and Micha Gorelick · 2017
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Learning non-discriminatory predictors
Blake W., Suriya G., Mesrob I O., and Nathan S · 2017
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Beyond parity: Fairness objectives for collaborative filtering
Sirui Yao and Bert Huang · 2017
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Fairness in machine learning: Lessons from political philosophy
Reuben Binns · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Fair clustering through fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii · 2018
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A fairness-aware hybrid recommender system
Golnoosh Farnadi, Pigi Kouki, Spencer K. Thompson, Sriram Srinivasan, and Lise Getoor · 2018
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Word embeddings quantify 100 years of gender and ethnic stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou · 2018
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Non-discriminatory machine learning through convex fairness criteria
Naman Goel, Mohammad Yaghini, and Boi Faltings · 2018
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Fairness through computationally-bounded awareness
Michael Kim, Omer Reingold, and Guy Rothblum · 2018
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Adaptive sensitive reweighting to mitigate bias in fairness-aware classification
Emmanouil Krasanakis, Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, and Yiannis Kompatsiaris · 2018
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An adversarial approach to improve long-tail performance in neural collaborative filtering
Adit Krishnan, Ashish Sharma, Aravind Sankar, and Hari Sundaram · 2018
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User fairness in recommender systems
Jurek L., Avishek A., and Megha K · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
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Personalizing fairness-aware re-ranking
Weiwen Liu and Robin Burke · 2018
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Mining point cloud local structures by kernel correlation and graph pooling
Yiru Shen, Chen Feng, Yaoqing Yang, and Dong Tian · 2018
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Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity
Mitigating bias in online microfinance platforms: A case study on kiva. org
Soumajyoti Sarkar and Hamidreza Alvari · 2020
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A general approach to fairness with optimal transport
Chiappa Silvia, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich, and Aslanides John · 2020
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Poisoning attacks on algorithmic fairness
David Solans, Battista Biggio, and Carlos Castillo · 2020
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Seeding network influence in biased networks and the benefits of diversity
Ana-Andreea Stoica, Jessy Xinyi Han, and Augustin Chaintreau · 2020
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Investigating and mitigating degree-related biases in graph convoltuional networks
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang · 2020
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Automated seismic source characterization using deep graph neural networks
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Ana-Andreea Stoica, Christopher Riederer, and Augustin Chaintreau · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Bridging the gap between theory and practice in influence maximization: Raising awareness about hiv among homeless youth
Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo · 2018
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Algorithmic regulation: A critical interrogation
Karen Yeung · 2018
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The unfairness of popularity bias in recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher · 2019
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On the fairness of time-critical influence maximization in social networks
Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P Gummadi, and Adish Singla · 2019
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Fair treatment allocations in social networks
James Atwood, Hansa Srinivasan, Yoni Halpern, and David Sculley · 2019
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Martijn PA van den Ende and J-P Ampuero · 2020
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Addressing marketing bias in product recommendations
Mengting Wan, Jianmo Ni, Rishabh Misra, and Julian J. McAuley · 2020
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Democratizing algorithmic fairness
Pak-Hang Wong · 2020
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Towards a unified framework for fair and stable graph representation learning
Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik · 2021
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Molecular generative graph neural networks for drug discovery
Pietro Bongini, Monica Bianchini, and Franco Scarselli · 2021
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Certification and trade-off of multiple fairness criteria in graph-based spam detection
Kai Burkholder, Kenny Kwock, Yuesheng Xu, Jiaxin Liu, Chao Chen, and Sihong Xie · 2021
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The kl-divergence between a graph model and its fair i-projection as a fairness regularizer
Maarten Buyl and Tijl De Bie · 2021
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Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Enyan Dai and Suhang Wang · 2021
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Individual fairness for graph neural networks: A ranking based approach
Yushun Dong, Jian Kang, Hanghang Tong, and Jundong Li · 2021
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Fair graph auto-encoder for unbiased graph representations with wasserstein distance
Wei Fan, Kunpeng Liu, Rui Xie, Hao Liu, Hui Xiong, and Yanjie Fu · 2021
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Combining graph neural networks and spatio-temporal disease models to predict covid-19 cases in germany
Cornelius Fritz, Emilio Dorigatti, and David Rügamer · 2021
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Towards long-term fairness in recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, and Yongfeng Zhang · 2021
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Protecting individual interests across clusters: Spectral clustering with guarantees
Shubham Gupta and Ambedkar Dukkipati · 2021
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Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo · 2021
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Generalized demographic parity for group fairness
Zhimeng Jiang, Xiaotian Han, Chao Fan, Fan Yang, Ali Mostafavi, and Xia Hu · 2021
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Fair graph mining
Jian Kang and Hanghang Tong · 2021
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Fairness-aware node representation learning
Öykü Deniz Köse and Yanning Shen · 2021
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All of the fairness for edge prediction with optimal transport
Charlotte Laclau, Ievgen Redko, Manvi Choudhary, and Christine Largeron · 2021
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On dyadic fairness: Exploring and mitigating bias in graph connections
Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, and Hongfu Liu · 2021
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User-oriented fairness in recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2021
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A survey on bias and fairness in machine learning
Ninareh M., Fred M., Nripsuta S., Kristina L., and Aram G · 2021
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Subgroup generalization and fairness of graph neural networks
Jiaqi Ma, Junwei Deng, and Qiaozhu Mei · 2021
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Fairness-preserving group recommendations with user weighting
Ladislav Malecek and Ladislav Peska · 2021
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Exacerbating algorithmic bias through fairness attacks
Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, and Aram Galstyan · 2021
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Algorithmic fairness: Choices, assumptions, and definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum · 2021
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Investigating trade-offs in utility, fairness and differential privacy in neural networks
Marlotte Pannekoek and Giacomo Spigler · 2021
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Fairness in rankings and recommendations: An overview
Evaggelia Pitoura, Kostas Stefanidis, and Georgia Koutrika · 2021
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Fair influence maximization: A welfare optimization approach
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, and Milind Tambe · 2021
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Hm-eiict: Fairness-aware link prediction in complex networks using community information
Akrati Saxena, George Fletcher, and Mykola Pechenizkiy · 2021
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Biased edge dropout for enhancing fairness in graph representation learning
Indro Spinelli, Simone Scardapane, Amir Hussain, and Aurelio Uncini · 2021
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On influencing the influential: disparity seeding
Ya-Wen Teng, Hsi-Wen Chen, De-Nian Yang, Yvonne-Anne Pignolet, Ting-Wei Li, and Lydia Chen · 2021
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Fairness-aware pagerank
Sotiris Tsioutsiouliklis, Evaggelia Pitoura, Panayiotis Tsaparas, Ilias Kleftakis, and Nikos Mamoulis · 2021
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Fairness-aware news recommendation with decomposed adversarial learning
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie · 2021
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Learning fair representations for recommendation: A graph-based perspective
Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang · 2021
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Fairrankvis: A visual analytics framework for exploring algorithmic fairness in graph mining models
Tiankai Xie, Yuxin Ma, Jian Kang, Hanghang Tong, and Ross Maciejewski · 2021
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Fairness-aware unsupervised feature selection
Xiaoying Xing, Hongfu Liu, Chen Chen, and Jundong Li · 2021
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Graph neural networks for automated de novo drug design
Jiacheng Xiong, Zhaoping Xiong, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng · 2021
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Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field
B. Xu, H. Shen, B. Sun, et al · 2021
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Fair representation learning in knowledge-aware recommendation
Bingke Xu, Yue Cui, Zipeng Sun, Liwei Deng, and Kai Zheng · 2021
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Fair representation learning for heterogeneous information networks
Ziqian Zeng, Rashidul Islam, et al · 2021
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A multi-view confidence-calibrated framework for fair and stable graph representation learning
Xu Zhang, Liang Zhang, Bo Jin, and Xinjiang Lu · 2021
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Popularity-opportunity bias in collaborative filtering
Ziwei Zhu, Yun He, et al · 2021
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Fairmod: Fair link prediction and recommendation via graph modification
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EDITS: modeling and mitigating data bias for graph neural networks
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Fmp: Toward fair graph message passing against topology bias
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Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
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Dual constraints and adversarial learning for fair recommenders
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Fairedit: Preserving fairness in graph neural networks through greedy graph editing
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