Research map / Graph learning / GNN architectures
Learning Signed Network Embedding via Graph Attention
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add balance-theory multi-layer aggregation (vs first attentional layer only) (ablation) | SNEA · Slashdot | AUC: 0.726 -> 0.799 | Helped |
| add balance-theory multi-layer aggregation (vs first attentional layer only) (ablation) | SNEA · Bit.Alpha | F1: 0.884 -> 0.927 | Helped |
| graph attention with balance theory vs SGCN | signed network embedding · Epinions | AUC: 0.849 -> 0.861 | Helped |
| set balance loss weight lambda=0 (ablation) | SNEA · Bit.Alpha | AUC | Hurt |
| increase lambda above 1 (ablation) | SNEA · Bit.Alpha | F1 | Helped |
| increase lambda above 1 (ablation) | SNEA · Bit.Alpha | AUC | No clear effect |
| SNEA initialized from TSVD vs TSVD embeddings | TSVD · Bit.Alpha | AUC: 0.740 -> 0.816 | Helped |
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