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 emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Recent studies show that graph convolutional network (GCN) often performs worse for low-
degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed …

Graph machine learning algorithms have become popular tools in helping us gain a deeper
understanding of the ubiquitous graph data. Despite their effectiveness, most graph machine …

Conventional graph neural networks (GNNs) are often confronted with fairness issues that
may stem from their input, including node attributes and neighbors surrounding a node …

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 …

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many
real-world applications. The vast majority of existing works on GCN primarily focus on the …

Dynamic graphs are ubiquitous across disciplines where observations usually change over
time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as …

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

Abstract In recent years, Graph Neural Networks (GNNs), an emerging technology for
learning from graph-structured data, have attracted much attention. Despite the widespread …