TU Dortmund
REDDIT-BINARY
Structure
2,000Graphs
430Nodes
498Edges
2Classes
Coverage
56Results
1Variants
7Papers
- Torch Geometric Pool: the Pytorch library for pooling in Graph Neural Networks Torch Geometric Pool: the Pytorch library for pooling in Graph Neural Networks
- Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks
- TMLR 2024: Graph Pooling via Ricci Flow Graph Pooling via Ricci Flow TMLR 2024
- ICLR 2023: Locality-Aware Graph Rewiring in GNNs Locality-Aware Graph Rewiring in GNNs ICLR 2023
- NeurIPS 2023: Fine-grained Expressivity of Graph Neural Networks Fine-grained Expressivity of Graph Neural Networks NeurIPS 2023
- ICLR 2022: Spectral Augmentation for Self-Supervised Learning on Graphs Spectral Augmentation for Self-Supervised Learning on Graphs ICLR 2022
- ICML 2020: Contrastive Multi-View Representation Learning on Graphs Contrastive Multi-View Representation Learning on Graphs ICML 2020
2-class graph classification Binary graph classification of Reddit discussion threads (question-answering vs. online discussion). Uses the released node/edge labels or attributes; several social datasets are featureless. Evaluated by Accuracy.
Dataset details
Task
Binary graph classification of Reddit discussion threads (question-answering vs. online discussion). The primary catalog metric is Accuracy.
Data
Binary graph classification of Reddit discussion threads (question-answering vs. online discussion). The cataloged artifact reports 2,000 graphs, 2 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 | GIN-0 | 0.9240
±
0.0250 | |
| 2 | GIN-ε | 0.9220
±
0.0230 | |
| 3 | bnpool | 0.9100
±
0.0200 | |
| 4 | DMoN | 0.9100
±
0.0100 | |
| 5 | hosc | 0.9100
±
0.0100 | |
| 6 | jbgnn | 0.9100
±
0.0200 | |
| 7 | kmis | 0.9100
±
0.0300 | |
| 8 | acc | 0.9000
±
0.0300 | |
| 9 | Diff | 0.9000
±
0.0200 | |
| 10 | ecpool | 0.9000
±
0.0200 | |
| 11 | eigen | 0.9000
±
0.0300 | |
| 12 | Graclus | 0.9000
±
0.0200 | |
| 13 | lapool | 0.9000
±
0.0100 | |
| 14 | MinCut | 0.9000
±
0.0200 | |
| 15 | GIN-m (trained) | 0.8973
±
0.0037 | |
| 16 | SEP | 0.8900
±
0.0300 | |
| 17 | AD-GCL | 0.8872
±
0.0153 | |
| 18 | GCL-SPAN | 0.8841
±
0.0112 | |
| 19 | ORC | 0.8800
±
0.0200 | |
| 20 | ASAP | 0.8800
±
0.0300 | |
| 21 | JOAO | 0.8765
±
0.0172 | |
| 22 | maxcut | 0.8700
±
0.0300 | |
| 23 | ndp | 0.8700
±
0.0200 | |
| 24 | pan | 0.8700
±
0.0500 | |
| 25 | sag | 0.8700
±
0.0300 | |
| 26 | No-Pre-Train-GIN | 0.8679
±
0.0204 | |
| 27 | MVGRL | 0.8624
±
0.0126 | |
| 28 | Diff | 0.8600
±
0.0200 | |
| 29 | topk | 0.8600
±
0.0300 | |
| 30 | FoSR | 0.8593
±
0.0279 | |
| 31 | GTR | 0.8570
±
0.0279 | |
| 32 | LVN | 0.8562
±
0.0099 | |
| 33 | LASER | 0.8546
±
0.0283 | |
| 34 | GAT | 0.8520
±
0.0330 | |
| 35 | MinCut | 0.8500
±
0.0200 | |
| 36 | DMoN | 0.8500
±
0.0200 | |
| 37 | TV | 0.8500
±
0.0300 | |
| 38 | BORF | 0.8492
±
0.0253 | |
| 39 | MI | 0.8450
±
0.0060 | |
| 40 | SDRF | 0.8442
±
0.0278 | |
| 41 | InfoGraph | 0.8250
±
0.0140 | |
| 42 | GraphConv-m (trained) | 0.8240
±
0.0019 | |
| 43 | None | 0.8100
±
0.0272 | |
| 44 | PANDA | 0.8008
±
0.0124 | |
| 45 | DGK | 0.7800
±
0.0040 | |
| 46 | GK | 0.7730
±
0.0020 | |
| 47 | graph2vec | 0.7580
±
0.0100 | |
| 48 | Sub2Vec | 0.7150
±
0.0040 | |
| 49 | FoSR | 0.7002
±
0.0091 | |
| 50 | WL | 0.6880
±
0.0040 |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time