Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph
representation learning. However, their efficacy within an end-to-end supervised framework …

Supervised fairness-aware machine learning under distribution shifts is an emerging field
that addresses the challenge of maintaining equitable and unbiased predictions when faced …

Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning
from complex data structured as graphs, demonstrating remarkable effectiveness in various …

Over the last few decades, machine learning (ML) applications have grown exponentially,
yielding several benefits to society. However, these benefits are tempered with concerns of …

Graph Neural Networks (GNNs) have become essential tools for graph representation
learning in various domains, such as social media and healthcare. However, they often …