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Found 9 papers, 6 papers with code
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests.
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation
While recommender systems have been designed to improve the user experience in dating platforms by providing personalized recommendations, increasing concerns about fairness have encouraged the development of fairness-aware recommender systems from various perspectives (e. g., gender and race).
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors.
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
Fairness and Diversity in Recommender Systems: A Survey
Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level.
Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations
Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure.
Collaboration-Aware Graph Convolutional Network for Recommender Systems
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect.
Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs.
