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 …

Machine learning over graphs has recently attracted growing attention due to its ability to
analyze and learn complex relations within critical interconnected systems. However, the …

The field of attributed graph clustering has garnered increasing attention, particularly with
the advent of graph convolutional network (GCN), which have deepened our understanding …

Graph Neural Networks (GNNs) have demonstrated strong performance in graph
representation learning across various real-world applications. However, they often produce …

The persistent challenge of bias in machine learning models necessitates robust solutions to
ensure parity and equal treatment across diverse groups, particularly in classification tasks …

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

Graph Neural Networks (GNNs) often struggle to generalize when graphs exhibit both
homophily (same-class connections) and heterophily (different-class connections) …