Research map / Graph learning / Knowledge and temporal graphs
Leveraging Node Attributes for Incomplete Relational Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use node attributes as priors for latent factor loadings | Sym-NARM · Undirected networks, especially with limited training data | Link prediction AUC-ROC and AUC-PR | Helped |
| Use node attributes as priors for latent factor loadings | Asym-NARM · Lazega-advice | Link prediction AUC-ROC and AUC-PR | Helped |
| Use node attributes | Directed relational models · Citeseer, Cora, and Aminer | Link prediction AUC-ROC and AUC-PR | Helped |
| Use more frequent words as attributes (ablation) | SSI-PF · Citeseer and Cora | Link prediction AUC-ROC and AUC-PR | Hurt |
| Use hierarchical attributes (ablation) | Asym-NARM · Cora-hier and Patent-hier | Link prediction AUC-ROC and AUC-PR | Helped |
| Increase training data beyond 30% | EPM · Facebook-ego | Link prediction performance relative to Sym-NARM | No clear effect |
| Use attributes with Asym-NARM rather than SSI-PF | Directed relational models · Citeseer, Cora, and Aminer | Link prediction AUC-ROC and AUC-PR | Helped |
| Use node attributes rather than no attributes | Directed relational models · Directed network datasets | Link prediction AUC-ROC and AUC-PR | Helped |
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