TU Dortmund
REDDIT-MULTI-5K
Structure
4,999Graphs
509Nodes
595Edges
5Classes
Coverage
42Results
1Variants
6Papers
- SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network
- SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
- GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling
- Unifying Graph Contrastive Learning via Graph Message Augmentation Unifying Graph Contrastive Learning via Graph Message Augmentation
- GIMM: InfoMin-Max for Automated Graph Contrastive Learning GIMM: InfoMin-Max for Automated Graph Contrastive Learning
- Reinforced Causal Explainer for Graph Neural Networks Reinforced Causal Explainer for Graph Neural Networks
5-class graph classification Multi-class graph classification of Reddit discussion threads (5 subreddits). Uses the released node/edge labels or attributes; several social datasets are featureless. Evaluated by Accuracy.
Dataset details
Task
Multi-class graph classification of Reddit discussion threads (5 subreddits). The primary catalog metric is Accuracy.
Data
Multi-class graph classification of Reddit discussion threads (5 subreddits). The cataloged artifact reports 4,999 graphs, 5 target classes or tasks.
Features
The TU text format may contain discrete node or edge labels and optional continuous attributes. Social-network datasets commonly have no intrinsic node attributes, so papers often add constant or degree features; that choice changes the input.
Splits and evaluation
The catalog records these protocols or variants: 10-fold CV. Evaluation uses Accuracy (higher is better). Exact masks or folds must come from the cited release.
Quirks and pitfalls
TU datasets have no canonical train/test split. Report cross-validation folds, seeds, feature construction, and whether duplicate or isomorphic graphs were removed; published results can otherwise differ for protocol rather than model quality.
Sources
SOTA Progression
new SOTA
matches SOTA at that time
other proposed result
other baseline result
Leaderboard
| # | Model | Accuracy ↑ | Paper |
|---|---|---|---|
| 1 | SpeGCL | 0.5944
±
0.0018 | |
| 2 | GIN | 0.5760
±
0.0150 | |
| 3 | LAMP-Soft | 0.5738
±
0.0041 | |
| 4 | GCS | 0.5704
±
0.0049 | |
| 5 | GMCL-M | 0.5681
±
0.0250 | |
| 6 | AutoGCL | 0.5675
±
0.0018 | |
| 7 | GMCL-D | 0.5663
±
0.0186 | |
| 8 | GMCL-P | 0.5635
±
0.0231 | |
| 9 | GPS-TopK | 0.5630
±
0.0020 | |
| 10 | SEGA | 0.5613
±
0.0030 | |
| 11 | GraphCL | 0.5599
±
0.0028 | |
| 12 | SimGRACE | 0.5590
±
0.0040 | |
| 13 | GPS-Cluster | 0.5590
±
0.0040 | |
| 14 | GRACE | 0.5571
±
0.0083 | |
| 15 | JOAO | 0.5570
±
0.0060 | |
| 16 | AD-GCL | 0.5490
±
0.0040 | |
| 17 | GIMM-Un-Fit | 0.5396
±
0.0051 | |
| 18 | Infomax | 0.5361
±
0.0031 | |
| 19 | AD-GCL | 0.5349
±
0.0028 | |
| 20 | InfoGraph | 0.5346
±
0.0103 | |
| 21 | JOAOv2 | 0.5271
±
0.0028 | |
| 22 | AutoGCL | 0.4991
±
0.0270 | |
| 23 | Original | 0.4920 | |
| 24 | Coarsened | 0.4810 | |
| 25 | GraphCL | 0.4809
±
0.0174 | |
| 26 | graph2vec | 0.4786
±
0.0026 | |
| 27 | RC-Explainer | 0.4720 | |
| 28 | WL | 0.4606
±
0.0021 | |
| 29 | JOAOv2 | 0.4557
±
0.0286 | |
| 30 | GraphSAGE | 0.4380
±
0.0320 | |
| 31 | DGK | 0.4127
±
0.0018 | |
| 32 | GL | 0.4101
±
0.0017 | |
| 33 | GK | 0.4100
±
0.0020 | |
| 34 | SP | 0.3960
±
0.0020 | |
| 35 | Sub2Vec | 0.3668
±
0.0042 | |
| 36 | GCA | 0.3295
±
0.1089 | |
| 37 | PGM-Explainer | 0.3000 | |
| 38 | CXPlain | 0.2790 | |
| 39 | PGExplainer | 0.1750 | |
| 40 | SA | 0.1580 | |
| 41 | Grad-CAM | 0.1580 | |
| 42 | GNNExplainer | 0.1250 |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time