Graph Clustering on Pubmed
35.77NMISCISE
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| SCISE2026.07 | 35.77 | 72.38 | 36.33 | 71.83 | |
| BGRLCategory=Standard GCL2024.10 | 34 | — | — | — | |
| GiGaMAECategory=Feature-based2024.10 | 34 | — | — | — | |
| GraphMAE2Category=Feature-based2024.10 | 33.9 | — | — | — | |
| EPAGCLCategory=Standard GCL2024.10 | 33.8 | — | — | — | |
| R-DGAE2021.07 | 33.6 | 71 | 33.9 | — | |
| mWDKMethod Category=Optimization-free (OpF) methods2026.02 | 33.5 | 69.9 | 32.5 | — | |
| S3GCLCategory=Standard GCL2024.10 | 33.5 | — | — | — | |
| AUG-MAECategory=Feature-based2024.10 | 33.5 | — | — | — | |
| PFGC2024.03 | 33.3 | 72.89 | — | — | |
| S3GC2026.07 | 33.3 | 71.3 | 34.5 | 70.3 | |
| CCA-SSGCategory=Standard GCL2024.10 | 33.2 | — | — | — | |
| GraphMAECategory=Feature-based2024.10 | 33.2 | — | — | — | |
| CGC2024.03 | 33.07 | 67.43 | — | — | |
| GRACECategory=Standard GCL2024.10 | 32.5 | — | — | — | |
| MCGC2024.03 | 32.45 | 66.95 | — | — | |
| R-GMM-VGAE2021.07 | 32.2 | 72.8 | 35.7 | — | |
| DGI2026.07 | 32.2 | 65.7 | 29.2 | 65.4 | |
| AGCMethod Category=Optimization-free (OpF) methods2026.02 | 32.1 | 61.8 | 28.4 | — | |
| WDKMethod Category=Optimization-free (OpF) methods2026.02 | 32 | 61.8 | 29.2 | — | |
| GGDCategory=Standard GCL2024.10 | 32 | — | — | — | |
| WLMethod Category=Optimization-free (OpF) methods2026.02 | 31.6 | 62.1 | 28.1 | — | |
| FGC2024.03 | 31.56 | 70.01 | — | — | |
| PANEMethod Category=Deep learning and random-walk methods2026.02 | 31.1 | 63.5 | 31.3 | — | |
| DGICategory=Standard GCL2024.10 | 31 | — | — | — | |
| CCGC2024.03 | 30.92 | 68.06 | — | — | |
| NDLSMethod Category=Deep learning and random-walk methods, Approach=Optimization-free (OpF)2026.02 | 30.9 | 67.6 | 30.8 | — | |
| DeSE2026.07 | 30.82 | 67.47 | 29.47 | 67.36 | |
| lrGAE 8Category=Structure-based2024.10 | 30.7 | — | — | — | |
| MAGIMethod Category=Deep learning and random-walk methods, Inference Type=End-to-end clustering2026.02 | 30.6 | 63.3 | 20.7 | — | |
| DAEGCMethod Category=Deep learning and random-walk methods, Inference Type=End-to-end clustering2026.02 | 30.1 | 63.6 | 28.7 | — | |
| SeeGeraCategory=Feature-based2024.10 | 30.1 | — | — | — | |
| R-ARGAE2021.07 | 30 | 69.2 | 30.9 | — | |
| Dink-Net2026.07 | 29.93 | 67.29 | 29.29 | 66.96 | |
| R-VGAE2021.07 | 29.9 | 68.9 | 30.6 | — | |
| DMoN2026.07 | 29.8 | 61.99 | 24.52 | 62.17 | |
| LSEnet2026.07 | 29.67 | 58.98 | 27.9 | 59 | |
| SDCN2024.03 | 29.47 | 65.78 | — | — | |
| ARGAE2021.07 | 29.4 | 68 | 29.3 | — | |
| MAGI2026.07 | 29.37 | 66.23 | 29.25 | 65.11 | |
| GSNNMethod Category=Deep learning and random-walk methods, Approach=GAE2026.02 | 29.1 | 69.8 | 30.9 | — | |
| R-GAE2021.07 | 28.7 | 68 | 29.3 | — | |
| GMM-VGAE2021.07 | 28.7 | 70.6 | 32 | — | |
| SCGC2024.03 | 28.65 | 67.73 | — | — | |
| MaskGAECategory=Structure-based2024.10 | 28.6 | — | — | — | |
| VGAE2021.07 | 28.3 | 68.9 | 30.6 | — | |
| CONVERT2026.07 | 28.19 | 63.29 | 26.59 | 62.29 | |
| DGAE2021.07 | 28 | 67.8 | 28 | — | |
| lrGAE 6Category=Structure-based2024.10 | 28 | — | — | — | |
| GSNNMethod Category=Deep learning and random-walk methods, Approach=Optimization-free (OpF)2026.02 | 27.5 | 63.8 | 25.5 | — | |
| lrGAE 7Category=Structure-based2024.10 | 27.3 | — | — | — | |
| NDLSMethod Category=Deep learning and random-walk methods, Approach=GAE2026.02 | 26.9 | 64.2 | 24.6 | — | |
| SCGC2026.07 | 26.05 | 65.12 | 25.27 | 61.49 | |
| GAECategory=Structure-based2024.10 | 24.8 | — | — | — | |
| R-ARVGAE2021.07 | 23.7 | 65.73 | 24.9 | — | |
| GAE2021.07 | 23.3 | 63.7 | 22.7 | — | |
| ARVGAE2021.07 | 23.1 | 63.4 | 22.4 | — | |
| GAEMethod Category=Deep learning and random-walk methods2026.02 | 22.4 | 63.8 | 24.1 | — | |
| SUBLIME2026.07 | 18.68 | 58.33 | 16.16 | 58.69 | |
| S2GAECategory=Structure-based2024.10 | 8.9 | — | — | — | |
| GAE𝑓Category=Feature-based2024.10 | 4 | — | — | — |