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

UPFD-POL

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 84.62 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
2 GCNFN User Preference-aware Fake News Detection 83.71 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
3 UPFD-BiGCN User Preference-aware Fake News Detection 83.26 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
4 UPFD-GAT User Preference-aware Fake News Detection 82.81 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
5 UPFD-GCNFN User Preference-aware Fake News Detection 82.35 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
6 UPFD-GCN User Preference-aware Fake News Detection 81.90 2021-04-25 📦 safe-graph/GNN-FakeNews 📦 code-implementation1/Code9
7 GNNCL User Preference-aware Fake News Detection 60.18 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