torch_geometric.nn.conv.CGConv
- class CGConv(channels: Union[int, Tuple[int, int]], dim: int = 0, aggr: str = 'add', batch_norm: bool = False, bias: bool = True, **kwargs)[source]
Bases:
MessagePassingThe crystal graph convolutional operator from the “Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties” paper.
\[\mathbf{x}^{\prime}_i = \mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \sigma \left( \mathbf{z}_{i,j} \mathbf{W}_f + \mathbf{b}_f \right) \odot g \left( \mathbf{z}_{i,j} \mathbf{W}_s + \mathbf{b}_s \right)\]where \(\mathbf{z}_{i,j} = [ \mathbf{x}_i, \mathbf{x}_j, \mathbf{e}_{i,j} ]\) denotes the concatenation of central node features, neighboring node features and edge features. In addition, \(\sigma\) and \(g\) denote the sigmoid and softplus functions, respectively.
- Parameters:
channels (int or tuple) – Size of each input sample. A tuple corresponds to the sizes of source and target dimensionalities.
dim (int, optional) – Edge feature dimensionality. (default:
0)aggr (str, optional) – The aggregation operator to use (
"add","mean","max"). (default:"add")batch_norm (bool, optional) – If set to
True, will make use of batch normalization. (default:False)bias (bool, optional) – If set to
False, the layer will not learn an additive bias. (default:True)**kwargs (optional) – Additional arguments of
torch_geometric.nn.conv.MessagePassing.
- Shapes:
input: node features \((|\mathcal{V}|, F)\) 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 \((|\mathcal{V}|, F)\) or \((|\mathcal{V_t}|, F_{t})\) if bipartite