ing with local and global consistency (2004)
<https://citeseerx.ist.psu.edu/doc_view/pid/d74c37aabf2d5cae663007cbd8718175466aea8c>`_

Examples
--------
>>> import numpy as np
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelSpreading
>>> label_prop_model = LabelSpreading()
>>> iris = datasets.load_iris()
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
LabelSpreading(...)
Ú	spreadingr(