Node Classification on BITalpha
80.63Mean AccuracyCLDG++
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CLDG++Input=X, A, S, T, Training Paradigm=Unsupervised2026.05 | 80.63 | 72.87 | |
| CLDGInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 80.61 | 72.9 | |
| GATInput=X, A, Y, Training Paradigm=Supervised2026.05 | 80.21 | 71.4 | |
| CCA-SSGInput=X, A, Training Paradigm=Unsupervised2026.05 | 79.97 | 72.69 | |
| GraphSAGEInput=X, A, Y, Training Paradigm=Supervised2026.05 | 79.89 | 74.24 | |
| MNCIInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 79.04 | 71.8 | |
| DGIInput=X, A, Training Paradigm=Unsupervised2026.05 | 78.56 | 73.19 | |
| CAWInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 77.64 | 71.99 | |
| TGATInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 77.63 | 68.03 | |
| DySATInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 77.61 | 68.07 | |
| GRACEInput=X, A, Training Paradigm=Unsupervised2026.05 | 77.6 | 67.81 | |
| LPInput=A, Y, Training Paradigm=Supervised2026.05 | 76.97 | 67.18 | |
| GCNInput=X, A, Y, Training Paradigm=Supervised2026.05 | 76.19 | 73.65 | |
| MVGRLInput=X, A, S, Training Paradigm=Unsupervised2026.05 | 75.26 | 72.71 |