HetCRF combines masked autoencoding and contrastive learning in a dual-channel framework with two positive-sample augmentation strategies, improving heterogeneous graph node classification under sparse features.
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Learning Robust Heterogeneous Graph Representations via Contrastive-Reconstruction under Sparse Semantics
HetCRF combines masked autoencoding and contrastive learning in a dual-channel framework with two positive-sample augmentation strategies, improving heterogeneous graph node classification under sparse features.