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Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision.
Language models are few-shot learners
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Pre-training on large-scale heterogeneous graph
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Contrastive pre-training of GNNs on heterogeneous graphs
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