Graph Classification On Upfd Pol
Metriken
Accuracy (%)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| HGFND | 91.13± 1.89 | Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks |
| UPFD-SAGE | 84.62 | User Preference-aware Fake News Detection |
| GCNFN | 83.71 | User Preference-aware Fake News Detection |
| UPFD-BiGCN | 83.26 | User Preference-aware Fake News Detection |
| UPFD-GAT | 82.81 | User Preference-aware Fake News Detection |
| UPFD-GCNFN | 82.35 | User Preference-aware Fake News Detection |
| UPFD-GCN | 81.90 | User Preference-aware Fake News Detection |
| GNNCL | 60.18 | User Preference-aware Fake News Detection |
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