Rongkun Li

Graph Self-Supervised Learning: A Hybrid Approach Combining Contrastive and Generative Paradigms

Graph-structured data is ubiquitous, yet labeled graph data remains scarce and expensive, limiting the effectiveness of supervised graph neural networks (GNNs). To address this, self-supervised learning (SSL) has emerged as a promising paradigm to pre-train GNNs on unlabeled graphs. However, existing SSL methods typica...