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.
K (int) – The number of hops/layer.
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