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Found 3 papers, 1 papers with code
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance.
MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning
In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data.
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Albeit having gained significant progress lately, large-scale graph representation learning remains expensive to train and deploy for two main reasons: (i) the repetitive computation of multi-hop message passing and non-linearity in graph neural networks (GNNs); (ii) the computational cost of complex pairwise contrastive learning loss.
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