Node Classification On Am
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Path Tree | 86.77 | Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph | |
| RR-GCN-PPV | 84.65 | R-GCN: The R Could Stand for Random | |
| RDF2Vec+SVM | 88.33 | RDF2Vec: RDF Graph Embeddings and Their Applications | |
| R-GCN | 89.29 | Modeling Relational Data with Graph Convolutional Networks | |
| BoP | 92.41 | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | |
| RR-GCN-PPV-CUT | 84.8 | R-GCN: The R Could Stand for Random | |
| RR-GCN-PPV-CUT (Unimportant relations removed) | 91.31 | R-GCN: The R Could Stand for Random | |
| SCENE | 90.05 | SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks |
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