Sterling: Synergistic representation learning on bipartite graphs
A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …
Coin: Co-cluster infomax for bipartite graphs
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 …
most existing methods are designed for homogeneous graphs yet not tailored for bipartite …
X-GOAL: Multiplex heterogeneous graph prototypical contrastive learning
Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …
plenty of attention in both academia and industry. A fundamental challenge for graph …
Heterogeneous Contrastive Learning for Foundation Models and Beyond
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 …
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …
Debiased Graph Poisoning Attack via Contrastive Surrogate Objective
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 …
the performance of GNNs through imperceptible changes on the graph. However, we find …