Research map / Graph learning / Graph self-supervised learning
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Bi-GCN instead of PPC_RNN+CNN | Rumor detection · Weibo | accuracy: 0.916 -> 0.961 | Helped |
| Use Bi-GCN instead of SVM-TK | Rumor detection · Twitter15 | accuracy: 0.750 -> 0.886 | Helped |
| Use Bi-GCN instead of RvNN | Rumor detection · Twitter16 | accuracy: 0.737 -> 0.880 | Helped |
| Add root-feature concatenation at GCN layers (ablation) | Bi-GCN and its single-direction and undirected variants · Weibo, Twitter15, and Twitter16 | accuracy | Helped |
| Combine top-down and bottom-up graph representations (ablation) | Bi-GCN · Weibo, Twitter15, and Twitter16 | accuracy | Helped |
| Use only top-down or bottom-up representations instead of both (ablation) | Bi-GCN · Weibo, Twitter15, and Twitter16 | accuracy | Mixed |
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