LabelPropagation

class dgl.nn.pytorch.utils.LabelPropagation(k, alpha, norm_type='sym', clamp=True, normalize=False, reset=False)[source]

Bases: Module

Label Propagation from Learning from Labeled and Unlabeled Data with Label Propagation

Y(t+1)=αA~Y(t)+(1α)Y(0)

where unlabeled data is initially set to zero and inferred from labeled data via propagation. α is a weight parameter for balancing between updated labels and initial labels. A~ denotes the normalized adjacency matrix.

Parameters:
  • k (int) – The number of propagation steps.

  • alpha (float) – The α coefficient in range [0, 1].

  • norm_type (str, optional) –

    The type of normalization applied to the adjacency matrix, must be one of the following choices:

    • row: row-normalized adjacency as D1A

    • sym: symmetrically normalized adjacency as D1/2AD1/2

    Default: ‘sym’.

  • clamp (bool, optional) – A bool flag to indicate whether to clamp the labels to [0, 1] after propagation. Default: True.

  • normalize (bool, optional) – A bool flag to indicate whether to apply row-normalization after propagation. Default: False.

  • reset (bool, optional) – A bool flag to indicate whether to reset the known labels after each propagation step. Default: False.

Examples

>>> import torch
>>> import dgl
>>> from dgl.nn import LabelPropagation
>>> label_propagation = LabelPropagation(k=5, alpha=0.5, clamp=False, normalize=True)
>>> g = dgl.rand_graph(5, 10)
>>> labels = torch.tensor([0, 2, 1, 3, 0]).long()
>>> mask = torch.tensor([0, 1, 1, 1, 0]).bool()
>>> new_labels = label_propagation(g, labels, mask)
forward(g, labels, mask=None)[source]

Compute the label propagation process.

Parameters:
  • g (DGLGraph) – The input graph.

  • labels (torch.Tensor) –

    The input node labels. There are three cases supported.

    • A LongTensor of shape (N,1) or (N,) for node class labels in multiclass classification, where N is the number of nodes.

    • A LongTensor of shape (N,C) for one-hot encoding of node class labels in multiclass classification, where C is the number of classes.

    • A LongTensor of shape (N,L) for node labels in multilabel binary classification, where L is the number of labels.

  • mask (torch.Tensor) – The bool indicators of shape (N,) with True denoting labeled nodes. Default: None, indicating all nodes are labeled.

Returns:

The propagated node labels of shape (N,D) with float type, where D is the number of classes or labels.

Return type:

torch.Tensor