Table 10 Performance of GCNs with different numbers of layers in depth sensitivity Experiments.
From: Simple yet effective heuristic community detection with graph convolution network
| Â | Â | Min DBI | Max Q | Max NMI | Max ACC | Max F1 | Max ARI | Max FMI | Max SC | Topsis Score |
|---|---|---|---|---|---|---|---|---|---|---|
Citeseer | single-layer GCN | 0.583929 | 0.822334 | 0.385114 | 0.559663 | 0.504300 | 0.356296 | 0.459963 | 0.814350 | 0.515353 |
two-layer GCN | 0.484069 | 0.818214 | 0.377790 | 0.599940 | 0.568800 | 0.345601 | 0.456540 | 0.842465 | 0.492008 | |
three-layer GCN | 0.444321 | 0.816524 | 0.376340 | 0.617072 | 0.605500 | 0.336664 | 0.459849 | 0.792908 | 0.515290 | |
Acm | single-layer GCN | 0.684481 | 0.745318 | 0.620495 | 0.844300 | 0.670900 | 0.692292 | 0.788906 | 0.798375 | 0.518493 |
two-layer GCN | 0.589847 | 0.752684 | 0.474369 | 0.621157 | 0.524600 | 0.475670 | 0.626644 | 0.598632 | 0.507636 | |
three-layer GCN | 0.572061 | 0.751419 | 0.440557 | 0.606281 | 0.599700 | 0.435782 | 0.595400 | 0.670572 | 0.502263 | |
Amap | single-layer GCN | 0.377126 | 0.670459 | 0.646764 | 0.685882 | 0.684800 | 0.506579 | 0.607000 | 0.899968 | 0.791961 |
two-layer GCN | 0.536609 | 0.661113 | 0.641394 | 0.608366 | 0.629600 | 0.486151 | 0.604802 | 0.751231 | 0.251217 | |
three-layer GCN | 0.566904 | 0.659047 | 0.603202 | 0.599869 | 0.621300 | 0.468950 | 0.587254 | 0.692937 | 0.487811 | |
Cocs | single-layer GCN | 0.534169 | 0.646346 | 0.528356 | 0.624284 | 0.527100 | 0.508426 | 0.586879 | 0.823781 | 0.681961 |
two-layer GCN | 0.636651 | 0.614536 | 0.390344 | 0.522064 | 0.392900 | 0.382378 | 0.482495 | 0.544767 | 0.381217 | |
three-layer GCN | 0.479450 | 0.591030 | 0.289053 | 0.442481 | 0.337100 | 0.315236 | 0.411786 | 0.717388 | 0.327811 |