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networkx.algorithms.node_classification.lgc.local_and_global_consistency

local_and_global_consistency(G, alpha=0.99, max_iter=30, label_name='label')[source]

Node classification by Local and Global Consistency

Parameters

  • G (NetworkX Graph)

  • alpha (float) – Clamping factor

  • max_iter (int) – Maximum number of iterations allowed

  • label_name (string) – Name of target labels to predict

Raises

NetworkXError –

Returns

predicted – Array of predicted labels

Return type

array, shape = [n_samples]

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.local_and_global_consistency(G)
>>> predicted
['A', 'A', 'B', 'B']

References

Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328.