Research map / Graph learning / Graph self-supervised learning
Link Prediction via Subgraph Embedding-Based Convex Matrix Completion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learned subgraph embeddings with convex matrix completion instead of strongest baseline | SEMAC · F200 | AUC (%): 90.9 -> 92.4 | Helped |
| SEMAC vs best baseline (node2vec/GraRep) | SEMAC · PPI | AUC (%): 88.9 -> 91.3 | Helped |
| SEMAC vs best baseline | SEMAC · F1600 | AUC (%): 96.8 -> 97.6 | Helped |
| increase learning rate from 0.1 toward 0.2-0.3 (ablation) | SEMAC · F400 | AUC | Helped |
| increase learning rate to 0.4 or higher (ablation) | SEMAC · F400 | AUC | Hurt |
| regularization ratio lambda >= 0.1 (ablation) | SEMAC · F400 | AUC | Hurt |
| reduce regularization ratio to small values (ablation) | SEMAC · F400 | AUC | No clear effect |
| increase maximum depth D (ablation) | SEMAC · datasets | AUC | Helped |
Closest papers by meaning
- GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction (NeurIPS 2026 · similarity 0.89)
- Structural Information Enhanced Graph Representation for Link Prediction (AAAI 2024 · similarity 0.88)
- A Topological Perspective on Demystifying GNN-Based Link Prediction Performance (ICLR 2024 · similarity 0.88)
- How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings (ICLR 2025 · similarity 0.87)
- Pure Message Passing Can Estimate Common Neighbor for Link Prediction (NeurIPS 2024 · similarity 0.87)
- Link Prediction in Multilayer Networks via Cross-Network Embedding (AAAI 2024 · similarity 0.87)
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