ML Research Wiki / Benchmarks / Graph Classification / UPFD-GOS

UPFD-GOS

Graph Classification Benchmark

Performance Over Time

📊 Showing 8 results | 📏 Metric: Accuracy (%)

Top Performing Models

Rank Model Paper Accuracy (%) Date Code
1 UPFD-SAGE User Preference-aware Fake News Detection 97.54 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
2 UPFD-GAT User Preference-aware Fake News Detection 96.52 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
3 UPFD-GCNFN User Preference-aware Fake News Detection 96.11 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
4 GCNFN User Preference-aware Fake News Detection 95.90 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
5 UPFD-GCN User Preference-aware Fake News Detection 95.11 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
6 GNNCL User Preference-aware Fake News Detection 93.60 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
7 UPFD-BiGCN User Preference-aware Fake News Detection 91.27 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
8 HGFND Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks 0.00 2022-12-24 📦 ujeong1/IEEEBigdata22_HGFND

All Papers (8)

safe-graph/GNN-FakeNews code-implementation1/Code9
safe-graph/GNN-FakeNews code-implementation1/Code9
safe-graph/GNN-FakeNews code-implementation1/Code9
safe-graph/GNN-FakeNews code-implementation1/Code9
safe-graph/GNN-FakeNews code-implementation1/Code9
safe-graph/GNN-FakeNews code-implementation1/Code9
safe-graph/GNN-FakeNews code-implementation1/Code9
ujeong1/IEEEBigdata22_HGFND