torch_geometric.transforms.SIGN
- class SIGN(K: int)[source]
Bases:
BaseTransform
The Scalable Inception Graph Neural Network module (SIGN) from the “SIGN: Scalable Inception Graph Neural Networks” paper (functional name:
sign
), which precomputes the fixed representations.\[\mathbf{X}^{(i)} = {\left( \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \right)}^i \mathbf{X}\]for \(i \in \{ 1, \ldots, K \}\) and saves them in
data.x1
,data.x2
, …Note
Since intermediate node representations are pre-computed, this operator is able to scale well to large graphs via classic mini-batching. For an example of using SIGN, see examples/sign.py.
- Parameters:
K (int) – The number of hops/layer.