Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Despite the remarkable success of graph neural networks (GNNs) for graph representation
learning, they are generally built on the (unreliable) iid assumption across training and …

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph
structure related tasks such as node classification and graph classification. However, GNNs …

Prior studies using graph neural networks (GNNs) for image classification have focused on
graphs generated from a regular grid of pixels or similar-sized superpixels. In the latter, a …

Semantic segmentation is one of the significant tasks in understanding aerial images with
high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism …

Biomedical instance segmentation is vulnerable to complicated instance morphology,
resulting in over-merge and over-segmentation. Recent advanced methods apply …

Graph convolutional neural networks (GCNNs) have been successfully applied to a wide
range of problems, including low-dimensional Euclidean structural domains representing …

Abstract Self-explainable Graph Neural Networks (GNNs) provide explanations alongside
their predictions, making the model transparent and facilitating their wide adoption in high …

With increasingly cyber-attacks and intrusion techniques, the threat of network security has
become more and more serious. However, existing solutions are no longer sufficient in …

3D shape representation and its processing have substantial effects on 3D shape
recognition. The polygon mesh as a 3D shape representation has many advantages in …