{"list":"other","total":87,"rows":[{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"1607.06520","title":"Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings","created":"2016-07-21","cited_by_count":1312},{"id":"1608.07187","title":"Semantics derived automatically from language corpora contain human-like biases","created":"2016-08-25","cited_by_count":901},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"1703.06856","title":"Counterfactual Fairness","created":"2017-03-20","cited_by_count":804},{"id":"1602.01585","title":"Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering","created":"2016-02-04","cited_by_count":520},{"id":"2010.04053","title":"Fairness in Machine Learning: A Survey","created":"2020-10-04","cited_by_count":370},{"id":"2101.11174","title":"Graph Neural Network for Traffic Forecasting: A Survey","created":"2021-01-27","cited_by_count":261},{"id":"1711.08412","title":"Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes","created":"2017-11-22","cited_by_count":253},{"id":"2010.03240","title":"Bias and Debias in Recommender System: A Survey and Future Directions","created":"2020-10-07","cited_by_count":240},{"id":"1511.05897","title":"Censoring Representations with an Adversary","created":"2015-11-18","cited_by_count":192},{"id":"1802.05733","title":"Fair Clustering Through Fairlets","created":"2018-02-15","cited_by_count":176},{"id":"2009.01454","title":"Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information","created":"2020-09-03","cited_by_count":168},{"id":"2003.01171","title":"A Semi-supervised Graph Attentive Network for Financial Fraud Detection","created":"2020-02-28","cited_by_count":151},{"id":"1712.03586","title":"Fairness in Machine Learning: Lessons from Political Philosophy","created":"2017-12-10","cited_by_count":149},{"id":"1901.04966","title":"Identifying and Correcting Label Bias in Machine Learning","created":"2019-01-15","cited_by_count":146},{"id":"2001.09784","title":"Algorithmic Fairness","created":"2020-01-21","cited_by_count":143},{"id":"2102.13186","title":"Towards a Unified Framework for Fair and Stable Graph Representation Learning","created":"2021-02-25","cited_by_count":128},{"id":"1905.10674","title":"Compositional Fairness Constraints for Graph Embeddings","created":"2019-05-25","cited_by_count":126},{"id":"1912.06883","title":"On the Apparent Conflict Between Individual and Group Fairness","created":"2019-12-14","cited_by_count":126},{"id":"1908.08843","title":"Fairness in Deep Learning: A Computational Perspective","created":"2019-08-23","cited_by_count":123},{"id":"1301.6822","title":"Discrimination in Online Ad Delivery","created":"2013-01-29","cited_by_count":93},{"id":"1705.08804","title":"Beyond Parity: Fairness Objectives for Collaborative Filtering","created":"2017-05-24","cited_by_count":93},{"id":"2108.05233","title":"EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks","created":"2021-08-11","cited_by_count":92},{"id":"2012.07397","title":"Molecular graph generation with Graph Neural Networks","created":"2020-12-14","cited_by_count":80},{"id":"2002.10764","title":"FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms","created":"2020-02-25","cited_by_count":78},{"id":"2101.03584","title":"Towards Long-term Fairness in Recommendation","created":"2021-01-10","cited_by_count":77},{"id":"2003.05707","title":"Fairness by Learning Orthogonal Disentangled Representations","created":"2020-03-12","cited_by_count":76},{"id":"2104.10671","title":"User-oriented Fairness in Recommendation","created":"2021-04-21","cited_by_count":75},{"id":"2006.02046","title":"Fairness-Aware Explainable Recommendation over Knowledge Graphs","created":"2020-06-03","cited_by_count":74},{"id":"1901.08668","title":"Guarantees for Spectral Clustering with Fairness Constraints","created":"2019-01-24","cited_by_count":72},{"id":"1907.13286","title":"The Unfairness of Popularity Bias in Recommendation","created":"2019-07-31","cited_by_count":70},{"id":"2106.15535","title":"Subgroup Generalization and Fairness of Graph Neural Networks","created":"2021-06-29","cited_by_count":69},{"id":"1907.01439","title":"Operationalizing Individual Fairness with Pairwise Fair Representations","created":"2019-07-02","cited_by_count":64},{"id":"2104.05994","title":"Fairness in Rankings and Recommendations: An Overview","created":"2021-04-13","cited_by_count":62},{"id":"2004.07401","title":"Poisoning Attacks on Algorithmic Fairness","created":"2020-04-15","cited_by_count":59},{"id":"2201.03662","title":"Learning Fair Node Representations with Graph Counterfactual Fairness","created":"2022-01-10","cited_by_count":58},{"id":"1803.03239","title":"Fairness Through Computationally-Bounded Awareness","created":"2018-03-08","cited_by_count":55},{"id":"2002.11442","title":"DeBayes: a Bayesian Method for Debiasing Network Embeddings","created":"2020-02-26","cited_by_count":55},{"id":"2102.09140","title":"Learning Fair Representations for Recommendation: A Graph-based Perspective","created":"2021-02-18","cited_by_count":50},{"id":"2012.08723","title":"Exacerbating Algorithmic Bias through Fairness Attacks","created":"2020-12-16","cited_by_count":47},{"id":"2006.16742","title":"FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning","created":"2020-06-30","cited_by_count":44},{"id":"2010.16326","title":"All of the Fairness for Edge Prediction with Optimal Transport","created":"2020-10-30","cited_by_count":36},{"id":"2202.04187","title":"FMP: Toward Fair Graph Message Passing against Topology Bias","created":"2022-02-08","cited_by_count":35},{"id":"2105.02725","title":"CrossWalk: Fairness-enhanced Node Representation Learning","created":"2021-05-06","cited_by_count":34},{"id":"2005.10545","title":"ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance","created":"2020-05-21","cited_by_count":30},{"id":"2201.00140","title":"Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning","created":"2022-01-01","cited_by_count":29},{"id":"2104.08769","title":"Fair Representation Learning for Heterogeneous Information Networks","created":"2021-04-18","cited_by_count":28},{"id":"2110.13957","title":"Unbiased Graph Embedding with Biased Graph Observations","created":"2021-10-26","cited_by_count":28},{"id":"2201.08549","title":"Fair Node Representation Learning via Adaptive Data Augmentation","created":"2022-01-21","cited_by_count":27},{"id":"2202.13547","title":"RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network","created":"2022-02-28","cited_by_count":27},{"id":"1901.10443","title":"Improved Adversarial Learning for Fair Classification","created":"2019-01-29","cited_by_count":25},{"id":"1902.07823","title":"Stable and Fair Classification","created":"2019-02-21","cited_by_count":25},{"id":"1912.01799","title":"Addressing Marketing Bias in Product Recommendations","created":"2019-12-04","cited_by_count":23},{"id":"1807.06349","title":"User Fairness in Recommender Systems","created":"2018-07-17","cited_by_count":21},{"id":"1903.00967","title":"Group-Fairness in Influence Maximization","created":"2019-03-03","cited_by_count":21},{"id":"2202.07170","title":"Fairness Amidst Non-IID Graph Data: A Literature Review","created":"2022-02-15","cited_by_count":20},{"id":"2006.06865","title":"Exploring Algorithmic Fairness in Robust Graph Covering Problems","created":"2020-06-11","cited_by_count":19},{"id":"1809.09030","title":"A Fairness-aware Hybrid Recommender System","created":"2018-09-13","cited_by_count":18},{"id":"1912.02761","title":"Measuring Social Bias in Knowledge Graph Embeddings","created":"2019-12-05","cited_by_count":17},{"id":"2102.05975","title":"Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks","created":"2021-02-11","cited_by_count":17},{"id":"2103.01846","title":"The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer","created":"2021-03-02","cited_by_count":17},{"id":"1911.06935","title":"Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare","created":"2019-11-16","cited_by_count":16},{"id":"2006.16309","title":"Adversarial Learning for Debiasing Knowledge Graph Embeddings","created":"2020-06-29","cited_by_count":16},{"id":"2005.04074","title":"Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks","created":"2020-05-08","cited_by_count":15},{"id":"2006.04279","title":"Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems","created":"2020-06-07","cited_by_count":15},{"id":"2201.03681","title":"FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing","created":"2022-01-10","cited_by_count":15},{"id":"2106.05391","title":"Fairness-Aware Node Representation Learning","created":"2021-06-09","cited_by_count":14},{"id":"2006.07906","title":"Fair Influence Maximization: A Welfare Optimization Approach","created":"2020-06-14","cited_by_count":12},{"id":"1909.03290","title":"Pretrained AI Models: Performativity, Mobility, and Change","created":"2019-09-07","cited_by_count":11},{"id":"1909.11793","title":"MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit","created":"2019-09-25","cited_by_count":11},{"id":"1905.06618","title":"On the Fairness of Time-Critical Influence Maximization in Social Networks","created":"2019-05-16","cited_by_count":10},{"id":"2106.02216","title":"Fairness-Aware Unsupervised Feature Selection","created":"2021-06-04","cited_by_count":10},{"id":"1809.02921","title":"Personalizing Fairness-aware Re-ranking","created":"2018-09-09","cited_by_count":9},{"id":"1903.02047","title":"Gaps in Information Access in Social Networks","created":"2019-03-05","cited_by_count":9},{"id":"1911.05489","title":"Fair treatment allocations in social networks","created":"2019-11-01","cited_by_count":9},{"id":"2101.00661","title":"Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany","created":"2021-01-03","cited_by_count":7},{"id":"2006.12995","title":"Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.org","created":"2020-06-20","cited_by_count":4},{"id":"1706.09976","title":"The Authority of \"Fair\" in Machine Learning","created":"2017-06-29","cited_by_count":3},{"id":"2011.08946","title":"A Unified Seeding Framework","created":"2020-11-17","cited_by_count":3}],"next_cursor":null}