Research Article
Variational Approach for Learning Community Structures
Table 3
Experimental results on PolBlogs dataset.
| | NMI () | VI () | ACC () | Q () | CON () | TPR () |
| Spectral Clustering | 0.0014 | 1.1152 | 0.4828 | -0.0578 | 0.5585 | 0.7221 | Louvain | 0.6446 | 1.0839 | 0.9149 | 0.2987 | 0.8130 | 0.1922 | DeepWalk | 0.7367 | 1.0839 | 0.9543 | 0.0980 | 0.3873 | 0.6870 | node2vec | 0.7545 | 0.8613 | 0.9586 | 0.1011 | 0.3827 | 0.6863 |
| Stochastic Blockmodel | 0.0002 | 1.2957 | 0.4905 | -0.0235 | 0.5329 | 0.5657 | Stochastic Blockmodel (D.C) | 0.7145 | 0.8890 | 0.9496 | 0.4256 | 0.0730 | 0.8101 | VGAE∗ + -means | 0.7361 | 0.8750 | 0.9552 | 0.4238 | 0.0752 | 0.8089 | VGAECD∗ | 0.7583 | 0.8583 | 0.9601 | 0.4112 | 0.0880 | 0.7913 |
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