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Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

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networkx.algorithms.node_classification.hmn.harmonic_function¶

harmonic_function(G, max_iter=30, label_name='label')[source]¶

Node classification by Harmonic function

Parameters

G (NetworkX Graph)
max_iter (int) – maximum number of iterations allowed
label_name (string) – name of target labels to predict

Returns

predicted – Array of predicted labels

Return type

array, shape = [n_samples]

Raises

NetworkXError – If no nodes on G has label_name.

Examples

>>> from networkx.algorithms import node_classification
>>> G = nx.path_graph(4)
>>> G.nodes[0]["label"] = "A"
>>> G.nodes[3]["label"] = "B"
>>> G.nodes(data=True)
NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}})
>>> G.edges()
EdgeView([(0, 1), (1, 2), (2, 3)])
>>> predicted = node_classification.harmonic_function(G)
>>> predicted
['A', 'A', 'B', 'B']

References

Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August). Semi-supervised learning using gaussian fields and harmonic functions. In ICML (Vol. 3, pp. 912-919).