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 …

Graph Neural Networks (GNNs) have shown satisfying performance in various graph
analytical problems. Hence, they have become the de facto solution in a variety of decision …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Federated learning (FL) is a distributed machine learning paradigm that enables data
owners to collaborate on training models while preserving data privacy. As FL effectively …

Abstract Graph Neural Networks (GNNs) have shown satisfying performance on various
graph learning tasks. To achieve better fitting capability, most GNNs are with a large number …

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can
reduce the bias of predictions on any demographic group (eg, female) in graph-based …

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …

Analysis of the fairness of machine learning (ML) algorithms has attracted many researchers'
interest. Several studies have shown that ML methods produce a bias toward different …

Algorithmic fairness and explainability are foundational elements for achieving responsible
AI. In this paper, we focus on their interplay, a research area that is recently receiving …

Graph self-supervised learning is now a go-to method for pre-training graph foundation
models, including graph neural networks, graph transformers, and more recent large …