Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

The growing importance of understanding and addressing algorithmic bias in artificial
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …

Large pre-trained vision-language models (VLMs) reduce the time for develo** predictive
models for various vision-grounded language downstream tasks by providing rich …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

Abstract Graph Neural Network (GNN) models have been extensively researched and
utilised for extracting valuable insights from graph data. The performance of fairness …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

Graph neural networks (GNNs) have become pivotal in various critical decision-making
scenarios due to their exceptional performance. However, concerns have been raised that …

Fairness measurement is crucial for assessing algorithmic bias in various types of machine
learning (ML) models, including ones used for search relevance, recommendation …

Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful
GNN models have been widely deployed in many real-world applications. Nevertheless …