LabelPropagation๏ƒ

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

Bases: Module

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

\[\mathbf{Y}^{(t+1)} = \alpha \tilde{A} \mathbf{Y}^{(t)} + (1 - \alpha) \mathbf{Y}^{(0)}\]

where unlabeled data is initially set to zero and inferred from labeled data via propagation. \(\alpha\) is a weight parameter for balancing between updated labels and initial labels. \(\tilde{A}\) denotes the normalized adjacency matrix.

Parameters:

  • k (int) โ€“ The number of propagation steps.

  • alpha (float) โ€“ The \(\alpha\) coefficient in range [0, 1].

  • norm_type (str, optional) โ€“

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

    • row: row-normalized adjacency as \(D^{-1}A\)

    • sym: symmetrically normalized adjacency as \(D^{-1/2}AD^{-1/2}\)

    Default: โ€˜symโ€™.

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

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

  • reset (bool, optional) โ€“ 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]๏ƒ

Compute the label propagation process.

Parameters:

  • g (DGLGraph) โ€“ The input graph.

  • labels (torch.Tensor) โ€“

    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) โ€“ 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