High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs

The proposed HASH-CODE framework integrates graph neural networks and pretrained language models through five self-supervised optimization objectives to capture the hierarchical intrinsic data correlations within text-attributed graphs, and introduces an HFC-aware contrastive learning objective to learn more distinctive node embeddings.