Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …

Graphs have been widely used in data mining and machine learning due to their unique
representation of real-world objects and their interactions. As graphs are getting bigger and …

There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for
semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled …

Graph data have become increasingly important, and graph node clustering has emerged
as a fundamental task in data analysis. In recent years, graph node clustering has gradually …

Graphs are widely used to model interconnected entities and improve downstream
predictions in various real-world applications. However, real-world graphs nowadays are …

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases
towards the node degree: they usually perform satisfactorily on high-degree nodes with rich …

Abstract Out-of-distribution (OOD) generalization is an important issue for Graph Neural
Networks (GNNs). Recent works employ different graph editions to generate augmented …