As graph neural networks (GNNs) struggle with large-scale graphs due to high
computational demands, data distillation for graph data promises to alleviate this issue by …

Fairness in machine learning has attracted increasing attention in recent years. The fairness
methods improving algorithmic fairness for in-distribution data may not perform well under …

Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-
based learning problem, such as credit risk assessment in financial networks and fake news …

There has been significant progress in improving the performance of graph neural networks
(GNNs) through enhancements in graph data, model architecture design, and training …

This paper introduces the Fair Fairness Benchmark (\textsf {FFB}), a benchmarking
framework for in-processing group fairness methods. Ensuring fairness in machine learning …

Existing work on fairness modeling commonly assumes that sensitive attributes for all
instances are fully available, which may not be true in many real-world applications due to …

Fairness has become a crucial aspect in the development of trustworthy machine learning
algorithms. Current fairness metrics to measure the violation of demographic parity have the …

As a prevalence data structure in the real world, graphs have found extensive applications
ranging from modeling social networks to molecules. However, the existence of diverse …

The goal of uplift modeling is to recommend actions that optimize specific outcomes by
determining which entities should receive treatment. One common approach involves two …

Measures of algorithmic fairness are usually discussed in the context of binary decisions.
We extend the approach to continuous scores. So far, ROC-based measures have mainly …