Node classification is a fundamental task of Graph Neural Networks (GNNs). However, GNN
models tend to suffer from the class imbalance problem which deteriorates the …

Although existing graph self-supervised learning approaches have paid attention to the
directed nature of networks, they have often overlooked the ubiquitous scale-free attributes …

Graph-based message-passing neural networks (MPNNs) have achieved remarkable
success in both node and graph-level learning tasks. However, several identified problems …

Motivated in part by a problem in simulated tempering (a form of Markov chain Monte Carlo)
we seek to minimise, in a suitable sense, the time it takes a (regular) diffusion with …

Network classification plays a crucial role in various domains like social network analysis
and bioinformatics. While Graph Neural Networks (GNNs) have achieved significant …