torch_geometric.nn.conv.SGConv
- class SGConv(in_channels: int, out_channels: int, K: int = 1, cached: bool = False, add_self_loops: bool = True, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing来自“简化图卷积网络”论文的简单图卷积操作符。
\[\mathbf{X}^{\prime} = {\left(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X} \mathbf{\Theta},\]where \(\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}\) denotes the adjacency matrix with inserted self-loops and \(\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}\) its diagonal degree matrix. The adjacency matrix can include other values than
1representing edge weights via the optionaledge_weighttensor.- Parameters:
in_channels (int) – Size of each input sample, or
-1to derive the size from the first input(s) to the forward method.out_channels (int) – Size of each output sample.
K (int, optional) – Number of hops \(K\). (default:
1)cached (bool, optional) – 如果设置为
True，该层将在第一次执行时缓存 \({\left(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X}\) 的计算结果， 并在后续执行中使用缓存版本。 此参数应仅在传导学习场景中设置为True。（默认值：False）add_self_loops (bool, optional) – If set to
False, will not add self-loops to the input graph. (default:True)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 \((|\mathcal{V}|, F_{in})\), edge indices \((2, |\mathcal{E}|)\), edge weights \((|\mathcal{E}|)\) (optional)
output: node features \((|\mathcal{V}|, F_{out})\)