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The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.
Compositional fairness constraints for graph embeddings
Bose, A. J.; and Hamilton, W. L. 2019 · 1905
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Operationalizing individual fairness with pairwise fair representations
Lahoti, P.; Gummadi, K. P.; and Weikum, G. 2019 · 1907
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A survey on bias and fairness in machine learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2019 · 1908
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An analysis of the greedy algorithm for the submodular set covering problem
Wolsey, L. A. 1982 · 1982
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DeBayes: a Bayesian method for debiasing network embeddings
Buyl, M.; and De Bie, T. 2020 · 2002
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Mining knowledge-sharing sites for viral marketing
Richardson, M.; and Domingos, P. 2002 · 2002
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Maximizing the spread of influence through a social network
Kempe, D.; Kleinberg, J.; and Tardos, É. 2003 · 2003
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Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
Khajehnejad, M.; Rezaei, A. A.; Babaei, M.; Hoffmann, J.; Jalili, M.; and Weller, A. 2020 · 2005
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Maximizing influence in a competitive social network: a follower’s perspective
Carnes, T.; Nagarajan, C.; Wild, S. M.; and Van Zuylen, A. 2007 · 2007
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Measuring user influence in Twitter: the million follower fallacy
Cha, M.; Haddadi, H.; Benevenuto, F.; and Gummadi, K. P. 2010 · 2010
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Networks, crowds, and markets , volume 8
Easley, D.; Kleinberg, J.; et al. 2010 · 2010
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You are who you know: inferring user profiles in online social networks
Mislove, A.; Viswanath, B.; Gummadi, K. P.; and Druschel, P. 2010 · 2010
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On minimizing budget and time in influence propagation over social networks
Goyal, A.; Bonchi, F.; Lakshmanan, L. V.; and Venkatasubramanian, S. 2013 · 2013
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Efficient estimation of word representations in vector space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
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Learning fair representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
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Deepwalk: Online learning of social representations
Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014 · 2014
Structural deep network embedding
Wang, D.; Cui, P.; and Zhu, W. 2016 · 2016
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
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An intelligence in our image: The risks of bias and errors in artificial intelligence
Osoba, O. A.; and Welser IV, W. 2017 · 2017
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Diversity constraints in public housing allocation
Benabbou, N.; Chakraborty, M.; Ho, X.-V.; Sliwinski, J.; and Zick, Y. 2018 · 2018
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Learning optimal and fair decision trees for non-discriminative decision-making
Aghaei, S.; Azizi, M. J.; and Vayanos, P. 2019 · 2019
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SimNet: Similarity-based network embeddings with mean commute time
Khajehnejad, M. 2019 · 2019
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On the efficiency of the information networks in social media
Babaei, M.; Grabowicz, P.; Valera, I.; Gummadi, K. P.; and Gomez-Rodriguez, M. 2016 · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A.; and Leskovec, J. 2016 · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
Cited alongside, same era.
Machine Bias: ThereâĂŹs Software Used Across the Country to Predict Future Criminals. And itâĂŹs Biased Against Blacks.(May 2016)
Kirchner, J. L. L. J. A.; and Mattu, S. 2016 · 2016
Cited alongside, same era.
Fairwalk: Towards Fair Graph Embedding
Rahman, T. A.; Surma, B.; Backes, M.; and Zhang, Y. 2019 · 2019
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Influence maximization across heterogeneous interconnected networks based on deep learning
Keikha, M. M.; Rahgozar, M.; Asadpour, M.; and Abdollahi, M. F. 2020 · 2020
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Bursting the Filter Bubble: Fairness-Aware Network Link Prediction
Masrour, F.; Wilson, T.; Yan, H.; Tan, P.-N.; and Esfahanian, A. 2020 · 2020
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