Graph Representation Learning in Hyperbolic Space via Dual-Masked (2025.coling-main)
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| Challenge: | Existing MR-based methods do not fully consider deep node and structural information. |
| Approach: | They propose a graph dual-masked self-supervised graph representation learning framework in hyperbolic space that masks nodes and edges and performs node aggregation. |
| Outcome: | The proposed method is superior in downstream tasks such as node classification and link prediction. |
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