Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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 …

The Metaverse, a hypothetical virtual space where people interact with digital environments,
has gained attention as a transformative concept in various industries like education …

Abstract Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving
graph analytical problems in various real-world applications. Nevertheless, GNNs could …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …

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 Neural Networks (GNNs) are playing increasingly important roles in critical decision-
making scenarios due to their exceptional performance and end-to-end design. However …

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 …

Discrimination against minority groups within the banking sector has long resulted in
unequal treatment in financial services. Recent works in the general machine learning …

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 …