Node Classification on Bitcoinotc
65.68Mean AccuracyCLDG
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CLDGInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 65.68 | 54.74 | |
| CLDG++Input=X, A, S, T, Training Paradigm=Unsupervised2026.05 | 65.37 | 54.44 | |
| CAWInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 63.56 | 53.59 | |
| MNCIInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 63.49 | 54.66 | |
| CCA-SSGInput=X, A, Training Paradigm=Unsupervised2026.05 | 63.47 | 51.7 | |
| GraphSAGEInput=X, A, Y, Training Paradigm=Supervised2026.05 | 62.68 | 56.99 | |
| GATInput=X, A, Y, Training Paradigm=Supervised2026.05 | 62.59 | 48.34 | |
| TGATInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 62.53 | 49.53 | |
| GRACEInput=X, A, Training Paradigm=Unsupervised2026.05 | 62.49 | 48.06 | |
| DySATInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 62.01 | 48.7 | |
| DGIInput=X, A, Training Paradigm=Unsupervised2026.05 | 61.68 | 51.65 | |
| LPInput=A, Y, Training Paradigm=Supervised2026.05 | 60.24 | 50.28 | |
| CLDG++Input=X, A, S, T, Training Paradigm=Unsupervised2026.05 | 59.88 | 58.96 | |
| CAWInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 59.85 | 57.92 | |
| MVGRLInput=X, A, S, Training Paradigm=Unsupervised2026.05 | 59.62 | 53 | |
| GCNInput=X, A, Y, Training Paradigm=Supervised2026.05 | 59.24 | 50.52 | |
| CLDGInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 59.17 | 58.45 | |
| TGATInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 58.56 | 55.73 | |
| GraphSAGEInput=X, A, Y, Training Paradigm=Supervised2026.05 | 57.29 | 56.3 | |
| DGIInput=X, A, Training Paradigm=Unsupervised2026.05 | 56.67 | 55.34 | |
| CCA-SSGInput=X, A, Training Paradigm=Unsupervised2026.05 | 56.48 | 54.83 | |
| MVGRLInput=X, A, S, Training Paradigm=Unsupervised2026.05 | 55.31 | 54.54 | |
| MNCIInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 55.14 | 54.79 | |
| GCNInput=X, A, Y, Training Paradigm=Supervised2026.05 | 54.61 | 54.41 | |
| GRACEInput=X, A, Training Paradigm=Unsupervised2026.05 | 54.5 | 53.48 | |
| GATInput=X, A, Y, Training Paradigm=Supervised2026.05 | 52.46 | 50.6 | |
| DySATInput=X, A, T, Training Paradigm=Unsupervised2026.05 | 51.32 | 48.8 | |
| LPInput=A, Y, Training Paradigm=Supervised2026.05 | 41.97 | 31.25 |