Research map / Graph learning / Knowledge and temporal graphs
Inductive representation learning on temporal graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add functional time encoding to GAT or GraphSAGE (ablation) | GAT and GraphSAGE · Link prediction; Reddit, Wikipedia, Industrial | Accuracy | Helped |
| Use learned temporal attention weights instead of equal weights (ablation) | TGAT · Link prediction and dynamic node classification; all three datasets | Accuracy / AUC | Helped |
| Use functional time encoding rather than positional encoding (ablation) | TGAT · Link prediction | AP | Helped |
| Use inverse-timespan neighborhood sampling rather than uniform sampling (ablation) | TGAT neighborhood dropout · Link prediction | AP | Hurt |
| Stack two TGAT layers rather than one (ablation) | TGAT · Link prediction | Accuracy and AP | Helped |
| Use three attention heads rather than other tested head counts (ablation) | TGAT · Link prediction | Accuracy and AP | Helped |
| Use flow-based time encoding instead of the non-parametric encoding (ablation) | TGAT · Link prediction | AP | No clear effect |
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