Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Abstract Graph Contrastive Learning (GCL) has shown superior performance in
representation learning in graph-structured data. Despite their success, most existing GCL …

Unsupervised multiplex graph representation learning (UMGRL) has received increasing
interest, but few works simultaneously focused on the common and private information …

Graph learning has become essential in various domains, including recommendation
systems and social network analysis. Graph Neural Networks (GNNs) have emerged as …

Recently, despite the unprecedented success of large pre-trained visual-language models
(VLMs) on a wide range of downstream tasks, the real-world unsupervised domain …

Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing
GNNs' generalization ability will degrade when there exist distribution shifts between testing …

We study the problem of aligning large language models (LLMs) with human preference
data. Contrastive preference optimization has shown promising results in aligning LLMs with …

Abstract Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is vulnerable to …

Graph neural networks have shown great ability in representation (GNNs) learning on
graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent …