Graph Clustering On Cora
Metrics
ACC
ARI
F1
NMI
Precision
Results
Performance results of various models on this benchmark
Model Name | ACC | ARI | F1 | NMI | Precision | Paper Title | Repository |
|---|---|---|---|---|---|---|---|
| ARGE | 64 | 35.2 | 61.9 | 0.449 | 64.6 | Adversarially Regularized Graph Autoencoder for Graph Embedding | |
| DAEGC+GSCAN† | - | 49.6 | 71.7 | 52.4 | - | GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-Mass | |
| GAE | 59.6 | - | - | - | - | Variational Graph Auto-Encoders | |
| RWR-GAE | 66.9 | - | - | 48.1 | - | RWR-GAE: Random Walk Regularization for Graph Auto Encoders | |
| R-GMM-VGAE | 76.7 | 57.9 | - | 57.3 | - | Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering | |
| AGC | 68.92 | - | - | 53.68 | - | Attributed Graph Clustering via Adaptive Graph Convolution | |
| R-DGAE | 73.7 | 54.1 | - | 56.0 | - | Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering | |
| ARVGE | 63.8 | 37.4 | 62.7 | 45 | 62.4 | Adversarially Regularized Graph Autoencoder for Graph Embedding | |
| RWR-VGAE | 68.5 | - | - | 45.5 | - | RWR-GAE: Random Walk Regularization for Graph Auto Encoders |
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