Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Abstract Graph Transformers (GTs) have proved their advantage in graph-level tasks.
However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …

Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-
training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts …

The past few years have seen an increased interest in aerial image object detection due to
its critical value to large-scale geoscientific research like environmental studies, urban …

Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-
supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a …

Many neuropsychiatric disorders are considered to be associated with abnormalities in the
functional connectivity networks of the brain. The research on the classification of functional …

Abstract Graph Neural Networks (GNNs) have been successfully applied to graph-level
tasks in various fields such as biology, social networks, computer vision, and natural …

Functional brain networks represent dynamic and complex interactions among anatomical
regions of interest (ROIs), providing crucial clinical insights for neural pattern discovery and …

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks
(GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling …