class turicreate.label_propagation.LabelPropagationModel(model)

A LabelPropagationModel computes the probability of each class label for each unlabeled vertex.

For each labeled vertices, the probability for class k is fixed to:

\[Pr_i(label=k) = I(label[i] == k)\]

where \(I()\) is the indicator function.

For all unlabeled vertices, the probability for each class k is computed from applying the following update iteratively:

\[ \begin{align}\begin{aligned}Pr_i(label=k) = Pr_i(label=k) * W_0 + \sum_{j\in N(i)} Pr_j(label=k) * W(j,i)\\Pr_i = Normalize(Pr_i)\end{aligned}\end{align} \]

where \(N(i)\) is the set containing all vertices \(j\) such that there is an edge going from \(j\) to \(i\). \(W(j,i)\) is the edge weight from \(j\) to \(i\), and \(W_0\) is the weight for self edge.

In the above equation, the first term is the probability of keeping the label from the previous iteration, and the second term is the probability of transition to a neighbor’s label.

Repeated edges (i.e., multiple edges where the source vertices are the same and the destination vertices are the same) are treated like normal edges in the above recursion.

By default, the label propagates from source to target. But if undirected is set to true in turicreate.label_propagation.create(), then the label propagates in both directions for each edge.

Below is a list of queryable fields for this model:

Field Description
labels An SFrame with label probability for each vertex
graph A new SGraph with label probability as vertex properties
delta Average changes in label probability during the last iteration (avg. of the L2 norm of the changes)
num_iterations Number of iterations
training_time Total training time of the model
threshold The convergence threshold in average L2 norm
self_weight The weight for self edge
weight_field The edge weight field id
label_field The vertex label field id
undirected Treat edge as undirected

This model cannot be constructed directly. Instead, use turicreate.label_propagation.create() to create an instance of this model. A detailed list of parameter options and code samples are available in the documentation for the create function.

See also



LabelPropagationModel.name(self) Returns the name of the model.
LabelPropagationModel.save(self, location) Save the model.
LabelPropagationModel.summary(self[, output]) Print a summary of the model.