LabelPropagation

gtrick.dgl.LabelPropagation(num_layers, alpha)

Bases: nn.Module

The label propagation operator from the "Learning from Labeled and Unlabeled Datawith Label Propagation" paper.

This trick is helpful for Node Level Task.

\[ \mathbf{Y}^{\prime} = \alpha \cdot \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \mathbf{Y} + (1 - \\alpha) \mathbf{Y}, \]

where unlabeled data is inferred by labeled data via propagation.

Examples:

LabelPropagation (DGL)

Parameters:

Name	Type	Description	Default
num_layers	int	The number of propagations.	required
alpha	float	The \(\alpha\) coefficient.	required

forward(graph, y, mask=None, edge_weight=None, post_step=lambda y: y.clamp_(0.0, 1.0))

Parameters:

Name	Type	Description	Default
graph	dgl.DGLGraph	The graph.	required
y	torch.Tensor	The ground-truth label information of training nodes.	required
mask	torch.LongTensor or BoolTensor	A mask or index tensor denoting which nodes were used for training.	None
edge_weight	torch.Tensor	The edge weights.	None
post_step	Callable[[torch.Tensor], torch.Tensor]	The post-process function.	lambda y: y.clamp_(0.0, 1.0)

Returns:

Type	Description
torch.Tensor	The obtained prediction.