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 …
Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory …
There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training …
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the" …
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 …
J Blandin, IA Kash - The 2024 ACM Conference on Fairness …, 2024 - dl.acm.org
Defining fairness in algorithmic contexts is challenging, particularly when adapting to new domains. Our research introduces a novel method for learning and applying group fairness …
Recent advances in fair graph learning observe that graph neural networks (GNNs) further amplify prediction bias compared with multilayer perception (MLP), while the reason behind …
Deep reinforcement learning has recently achieved remarkable success in various domains, ranging from games [10, 11, 12], to real-world applications such as neural architecture …
Deep learning on graphs has garnered considerable attention across various machine learning applications, encompassing social science, transportation services, and biomedical …