Node Classification 벤치마크
Node Classification on BGS
Accuracy
- 2017-03-17 — R-GCN: Accuracy 83.1
- 2017-11-10 — RDF2Vec+SVM: Accuracy 87.24
- 2023-01-09 — SCENE: Accuracy 92.41
| Rank | Model | Accuracy | Paper | Code | Year |
|---|---|---|---|---|---|
| 1 | SCENE | 92.41 | SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks | schmidt-ju/scene | 2023 |
| 2 | BoP | 90.34 | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | kbogas/PAM_BoP | 2024 |
| 3 | RDF2Vec+SVM | 87.24 | RDF2Vec: RDF Graph Embeddings and Their Applications | IBCNServices/pyRDF2Vec | 2017 |
| 4 | Path Tree | 86.90 | Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph | IBCNServices/KGPTree | 2019 |
| 5 | RR-GCN-PPV-CUT | 84.14 | R-GCN: The R Could Stand for Random | predict-idlab/RR-GCN | 2022 |
| 6 | R-GCN | 83.10 | Modeling Relational Data with Graph Convolutional Networks | dmlc/dgl · dmlc/dgl · dmlc/dgl · +24 | 2017 |
| 7 | RR-GCN-PPV | 78.97 | R-GCN: The R Could Stand for Random | predict-idlab/RR-GCN | 2022 |