torch_geometric.transforms.SIGN

class SIGN(K: int)

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.