torch_geometric.nn.models.RECT_L
- class RECT_L(in_channels: int, hidden_channels: int, normalize: bool = True, dropout: float = 0.0)[source]
Bases:
Module
The RECT model, i.e. its supervised RECT-L part, from the “Network Embedding with Completely-imbalanced Labels” paper. In particular, a GCN model is trained that reconstructs semantic class knowledge.
Note
For an example of using RECT, see examples/rect.py.
- Parameters:
in_channels (int) – Size of each input sample.
hidden_channels (int) – Intermediate size of each sample.
normalize (bool, optional) – Whether to add self-loops and compute symmetric normalization coefficients on-the-fly. (default:
True
)dropout (float, optional) – The dropout probability. (default:
0.0
)