Research Article
Variational Approach for Learning Community Structures
Table 2
Experimental results on Karate dataset.
| | NMI () | VI () | ACC () | Q () | CON () | TPR () |
| Spectral Clustering | 0.2297 | 2.0005 | 0.7353 | 0.1127 | 0.3702 | 0.7363 | Louvain | 0.4900 | 1.5205 | 0.3235 | 0.4188 | 0.2879 | 0.7333 | DeepWalk | 0.7198 | 0.8812 | 0.9353 | 0.3582 | 0.1337 | 0.9353 | node2vec | 0.8372 | 0.8050 | 0.9706 | 0.1639 | 0.4239 | 0.4549 |
| Stochastic Blockmodel | 0.0105 | 1.1032 | 0.4412 | -0.2084 | 0.7154 | 0.4034 | Stochastic Blockmodel (D.C) | 0.8372 | 0.8050 | 0.9706 | 0.3718 | 0.1282 | 0.9412 | VGAE∗ + -means | 0.6486 | 0.8189 | 0.9647 | 0.3669 | 0.1295 | 0.9407 | VGAECD∗ | 1.0000 | 0.6931 | 1.0000 | 0.3582 | 0.1412 | 0.9412 |
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