Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …

Graph few-shot learning is of great importance among various graph learning tasks. Under
the few-shot scenario, models are often required to conduct classification given limited …

Pneumonia is a significant global health concern that can lead to severe and sometimes
fatal consequences. Timely identification and classification of pneumonia can substantially …

Data augmentations are effective in improving the invariance of learning machines. We
argue that the core challenge of data augmentations lies in designing data transformations …

Self-supervised representation learning on text-attributed graphs, which aims to create
expressive and generalizable representations for various downstream tasks, has received …

Abstract Graph Neural Networks (GNN) is one of the promising machine learning areas in
solving real world problems such as social networks, recommender systems, computer …

In knowledge graph representation learning, link prediction is among the most popular and
influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding …

This work studies self-supervised graph learning for text-attributed graphs (TAGs) where
nodes are represented by textual attributes. Unlike traditional graph contrastive methods that …

Forests are an invaluable natural resource, playing a crucial role in the regulation of both
local and global climate patterns. Additionally, they offer a plethora of benefits such as …

Graph Comparative Learning (GCL) is a self-supervised method that combines the
advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it …