Research map / Graph learning / Knowledge and temporal graphs
DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add temporal fusion units to combine graph representations stage by stage (ablation) | DDGCN · Weibo | ACC: 0.929 -> 0.948 | Helped |
| add temporal fusion units to combine graph representations stage by stage (ablation) | DDGCN · Pheme | ACC: 0.842 -> 0.855 | Helped |
| use dynamic rather than static GCNs (ablation) | DDGCN · Weibo | ACC: 0.909 -> 0.948 | Helped |
| use dynamic rather than static GCNs (ablation) | DDGCN · Pheme | ACC: 0.841 -> 0.855 | Helped |
| add dynamic knowledge GCNs (ablation) | DDGCN · Weibo | ACC: 0.922 -> 0.948 | Helped |
| add dynamic knowledge GCNs (ablation) | DDGCN · Pheme | ACC: 0.833 -> 0.855 | Helped |
| add dynamic propagation GCNs (ablation) | DDGCN · Weibo | ACC: 0.904 -> 0.948 | Helped |
| add dynamic propagation GCNs (ablation) | DDGCN · Pheme | ACC: 0.832 -> 0.855 | Helped |
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