torch_geometric.nn.norm.MessageNorm

class MessageNorm(learn_scale: bool = False)

Bases: Module

Applies message normalization over the aggregated messages as described in the “DeeperGCNs: All You Need to Train Deeper GCNs” paper.

\[\mathbf{x}_i^{\prime} = \mathrm{MLP} \left( \mathbf{x}_{i} + s \cdot {\| \mathbf{x}_i \|}_2 \cdot \frac{\mathbf{m}_{i}}{{\|\mathbf{m}_i\|}_2} \right)\]

Parameters:

learn_scale (bool, optional) – If set to True, will learn the scaling factor \(s\) of message normalization. (default: False)

reset_parameters()

Resets all learnable parameters of the module.

forward(x: Tensor, msg: Tensor, p: float = 2.0) → Tensor

Forward pass.

Parameters:

• x (torch.Tensor) – The source tensor.

• msg (torch.Tensor) – The message tensor \(\mathbf{M}\).

• p (float, optional) – The norm \(p\) to use for normalization. (default: 2.0)