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 …

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

Graph Neural Networks (GNNs) have shown great power in various domains. However, their
predictions may inherit societal biases on sensitive attributes, limiting their adoption in real …

Graphs are mathematical tools that can be used to represent complex real-world
interconnected systems, such as financial markets and social networks. Hence, machine …

In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …

Fueled by the soaring popularity of foundation models, the accelerated growth of artificial
intelligence (AI) models' enormous environmental footprint has come under increased …

Graphs can facilitate modeling of various complex systems and the analyses of the
underlying relations within them, such as gene networks and power grids. Hence, learning …

Graphs are mathematical tools that can be used to represent complex real-world systems,
such as financial markets and social networks. Hence, machine learning (ML) over graphs …

Many real-world networks such as social networks, traffic networks vary over time, which can
be modeled as dynamic graphs. Despite the significant number of systems that can facilitate …

We address the growing apprehension that GNNs, in the absence of fairness constraints,
might produce biased decisions that disproportionately affect underprivileged groups or …