Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

Contrastive learning (CL), which can extract the information shared between different
contrastive views, has become a popular paradigm for vision representation learning …

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

Recent years, graph contrastive learning (GCL), which aims to learn representations from
unlabeled graphs, has made great progress. However, the existing GCL methods mostly …

Continual graph learning routinely finds its role in a variety of real-world applications where
the graph data with different tasks come sequentially. Despite the success of prior works, it …

Graph contrastive learning has been successfully applied in text classification due to its
remarkable ability for self-supervised node representation learning. However, explicit graph …

Graphs are typical non-Euclidean data of complex structures. Recently, Riemannian graph
representation learning emerges as an exciting alternative to the traditional Euclidean ones …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

Representation learning on temporal graphs has drawn considerable research attention
owing to its fundamental importance in a wide spectrum of real-world applications. Though a …