Reddit Binary
Description
REDDIT-BINARY
Dataset Overview
Lifting Methods
Structural-based Liftings
- Cellular: Cycle-based lifting
- Simplicial: Clique complex lifting
- Hypergraph: k-hop lifting
Feature Lifting
- Projected sum
Model Performance
| Model | Accuracy (%) | Std Dev (±) |
|---|---|---|
| GCN | 76.24 | 0.54 |
| GIN | 81.96 | 1.36 |
| GAT | 75.68 | 1.00 |
| AST | 74.84 | 2.68 |
| EDGNN | 83.24 | 1.45 |
| UniGNN2 | 75.56 | 3.19 |
| CWN | 85.52 | 1.38 |
| CCCN | 85.12 | 1.29 |
| SCCNN | 77.24 | 1.87 |
| SCN | 71.28 | 2.06 |
Key Insights
- CWN achieves the best performance with 85.52% accuracy
- CCCN is the second best with 85.12% accuracy
- EDGNN also performs strongly with 83.24% accuracy
- GIN and CCCN show relatively high variability (±1.36, ±1.29)
- GCN and GAT have lower performance compared to other models
Reproducibility
python -m topobench model=cell/cwn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBench --multirun