A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …

Graph self-supervised learning has attracted plenty of attention in recent years. However,
most existing methods are designed for homogeneous graphs yet not tailored for bipartite …

Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Graph neural networks (GNN) are vulnerable to adversarial attacks, which aim to degrade
the performance of GNNs through imperceptible changes on the graph. However, we find …