Abstract Graph Neural Networks (GNNs) are widely employed to derive meaningful node
representations from graphs. Despite their success, deep GNNs frequently grapple with the …

Abstract Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture
tailored for graphs has shown notable potential for mitigating label scarcity. Its core idea is to …

Semi-supervised image classification, leveraging pseudo supervision and consistency
regularization, has demonstrated remarkable success. However, the ongoing challenge lies …

Existing multi-view graph learning methods often rely on consistent information for similar
nodes within and across views, however they may lack adaptability when facing diversity …

Graph node classification with few labeled nodes presents significant challenges due to
limited supervision. Conventional methods often exploit the graph in a transductive learning …

Recent researches have demonstrated pre-training Graph Neural Networks (GNNs) via
meta learning can enhance their performance on learning representations from unlabeled …

Graph representation learning aims to represent graphs as vectors that can be utilized in
downstream tasks such as graph classification. In this work, we focus on learning diverse …

Enlightened by the InfoMax principle, Graph Contrastive Learning (GCL) has achieved
remarkable performance in processing large amounts of unlabeled graph data. Due to the …