58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
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# -*- coding: utf-8 -*-
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# Copyright (C) 2011-2019 by
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# Julien Klaus <julien.klaus@uni-jena.de>
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# All rights reserved.
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# BSD license.
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# Copyright 2016-2019 NetworkX developers.
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# NetworkX is distributed under a BSD license
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#
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# Authors: Julien Klaus <julien.klaus@uni-jena.de>
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r"""Function for computing the moral graph of a directed graph."""
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import networkx as nx
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from networkx.utils import not_implemented_for
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import itertools
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__all__ = ['moral_graph']
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@not_implemented_for('undirected')
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def moral_graph(G):
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r"""Return the Moral Graph
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Returns the moralized graph of a given directed graph.
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Parameters
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----------
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G : NetworkX graph
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Directed graph
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Returns
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-------
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H : NetworkX graph
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The undirected moralized graph of G
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Notes
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------
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A moral graph is an undirected graph H = (V, E) generated from a
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directed Graph, where if a node has more than one parent node, edges
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between these parent nodes are inserted and all directed edges become
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undirected.
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https://en.wikipedia.org/wiki/Moral_graph
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References
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----------
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.. [1] Wray L. Buntine. 1995. Chain graphs for learning.
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In Proceedings of the Eleventh conference on Uncertainty
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in artificial intelligence (UAI'95)
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"""
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if G is None:
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raise ValueError("Expected NetworkX graph!")
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H = G.to_undirected()
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for preds in G.pred.values():
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predecessors_combinations = itertools.combinations(preds, r=2)
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H.add_edges_from(predecessors_combinations)
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return H
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