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 …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

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 …

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

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

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 …

In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …