Heterogeneous Node Classification On Dblp 2
Metrics
Macro-F1
Micro-F1
Results
Performance results of various models on this benchmark
Model Name | Macro-F1 | Micro-F1 | Paper Title | Repository |
|---|---|---|---|---|
| NARS | 94.18 | 94.61 | Scalable Graph Neural Networks for Heterogeneous Graphs | |
| HetSANN | 78.55 | 80.56 | An Attention-based Graph Neural Network for Heterogeneous Structural Learning | |
| Simple-HGN | 94.01 | 94.46 | Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks | |
| GTN | 93.52 | 93.97 | Graph Transformer Networks | |
| HGT | 93.01 | 93.49 | Heterogeneous Graph Transformer | |
| RGCN | 91.52 | 92.07 | Modeling Relational Data with Graph Convolutional Networks | |
| GAT | - | 93.39 | Graph Attention Networks | |
| SlotGAT | 94.95 | 95.31 | SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network | |
| RpHGNN | 95.23 | 95.55 | Efficient Heterogeneous Graph Learning via Random Projection | |
| SeHGNN | - | 95.24 | Simple and Efficient Heterogeneous Graph Neural Network | |
| GCN | 90.84 | 91.47 | Semi-Supervised Classification with Graph Convolutional Networks |
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