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Found 16 papers, 9 papers with code
Large Language Model-based Augmentation for Imbalanced Node Classification on Text-Attributed Graphs
Node classification on graphs often suffers from class imbalance, leading to biased predictions and significant risks in real-world applications.
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the underlying structural properties of the graph.
SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones.
Edge Classification on Graphs: New Directions in Topological Imbalance
Our empirical studies confirm that TE effectively measures local class distribution variance, and indicate that prioritizing edges with high TE values can help address the issue of topological imbalance.
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.
A Survey of Graph Unlearning
By laying a solid foundation and fostering continued progress, this survey seeks to inspire researchers to further advance the field of graph unlearning, thereby instilling confidence in the ethical growth of AI systems and reinforcing the responsible application of machine learning techniques in various domains.
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.
