Node Classification 벤치마크
Node Classification on MUTAG
Accuracy
- 2017-03-17 — R-GCN: Accuracy 73.23
- 2019-08-22 — Path Tree: Accuracy 73.82
- 2022-03-04 — RR-GCN-PPV: Accuracy 79.41
- 2024-11-17 — BoP: Accuracy 91.17
| Rank | Model | Accuracy | Paper | Code | Year |
|---|---|---|---|---|---|
| 1 | BoP | 91.17 | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | kbogas/PAM_BoP | 2024 |
| 2 | RR-GCN-PPV | 79.41 | R-GCN: The R Could Stand for Random | predict-idlab/RR-GCN | 2022 |
| 3 | SCENE | 75.44 | SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks | schmidt-ju/scene | 2023 |
| 4 | Path Tree | 73.82 | Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph | IBCNServices/KGPTree | 2019 |
| 5 | R-GCN | 73.23 | Modeling Relational Data with Graph Convolutional Networks | dmlc/dgl · dmlc/dgl · dmlc/dgl · +24 | 2017 |
| 6 | RDF2Vec+SVM | 67.20 | RDF2Vec: RDF Graph Embeddings and Their Applications | IBCNServices/pyRDF2Vec | 2017 |