LabelPropagationο
- class dgl.nn.pytorch.utils.LabelPropagation(k, alpha, norm_type='sym', clamp=True, normalize=False, reset=False)[source]ο
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) β The number of propagation steps.
alpha (float) β The \(\alpha\) coefficient in range [0, 1].
norm_type (str, optional) β
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: βsymβ.
clamp (bool, optional) β A bool flag to indicate whether to clamp the labels to [0, 1] after propagation. Default: True.
normalize (bool, optional) β A bool flag to indicate whether to apply row-normalization after propagation. Default: False.
reset (bool, optional) β 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]ο
Compute the label propagation process.
- Parameters:
g (DGLGraph) β The input graph.
labels (torch.Tensor) β
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) β 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