torch_geometric.nn.conv.GINEConv
- class GINEConv(nn: Module, eps: float = 0.0, train_eps: bool = False, edge_dim: Optional[int] = None, **kwargs)[source]
Bases:
MessagePassingThe modified
GINConvoperator from the “Strategies for Pre-training Graph Neural Networks” paper.\[\mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot \mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathrm{ReLU} ( \mathbf{x}_j + \mathbf{e}_{j,i} ) \right)\]that is able to incorporate edge features \(\mathbf{e}_{j,i}\) into the aggregation procedure.
- Parameters:
nn (torch.nn.Module) – A neural network \(h_{\mathbf{\Theta}}\) that maps node features
xof shape[-1, in_channels]to shape[-1, out_channels], e.g., defined bytorch.nn.Sequential.eps (float, optional) – (Initial) \(\epsilon\)-value. (default:
0.)train_eps (bool, optional) – If set to
True, \(\epsilon\) will be a trainable parameter. (default:False)edge_dim (int, optional) – Edge feature dimensionality. If set to
None, node and edge feature dimensionality is expected to match. Other-wise, edge features are linearly transformed to match node feature dimensionality. (default:None)**kwargs (optional) – Additional arguments of
torch_geometric.nn.conv.MessagePassing.
- Shapes:
input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge features \((|\mathcal{E}|, D)\) (optional)
output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite