58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
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r"""Function for computing the moral graph of a directed graph."""
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import itertools
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from networkx.utils import not_implemented_for
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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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Raises
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------
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NetworkXNotImplemented
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If `G` is undirected.
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Examples
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--------
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>>> G = nx.DiGraph([(1, 2), (2, 3), (2, 5), (3, 4), (4, 3)])
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>>> G_moral = nx.moral_graph(G)
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>>> G_moral.edges()
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EdgeView([(1, 2), (2, 3), (2, 5), (2, 4), (3, 4)])
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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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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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