Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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65 lines
1.8 KiB
65 lines
1.8 KiB
4 years ago
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"""
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Ego graph.
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"""
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__all__ = ["ego_graph"]
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import networkx as nx
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def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
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"""Returns induced subgraph of neighbors centered at node n within
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a given radius.
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Parameters
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----------
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G : graph
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A NetworkX Graph or DiGraph
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n : node
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A single node
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radius : number, optional
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Include all neighbors of distance<=radius from n.
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center : bool, optional
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If False, do not include center node in graph
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undirected : bool, optional
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If True use both in- and out-neighbors of directed graphs.
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distance : key, optional
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Use specified edge data key as distance. For example, setting
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distance='weight' will use the edge weight to measure the
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distance from the node n.
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Notes
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-----
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For directed graphs D this produces the "out" neighborhood
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or successors. If you want the neighborhood of predecessors
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first reverse the graph with D.reverse(). If you want both
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directions use the keyword argument undirected=True.
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Node, edge, and graph attributes are copied to the returned subgraph.
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"""
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if undirected:
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if distance is not None:
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sp, _ = nx.single_source_dijkstra(
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G.to_undirected(), n, cutoff=radius, weight=distance
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)
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else:
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sp = dict(
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nx.single_source_shortest_path_length(
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G.to_undirected(), n, cutoff=radius
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)
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)
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else:
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if distance is not None:
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sp, _ = nx.single_source_dijkstra(G, n, cutoff=radius, weight=distance)
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else:
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sp = dict(nx.single_source_shortest_path_length(G, n, cutoff=radius))
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H = G.subgraph(sp).copy()
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if not center:
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H.remove_node(n)
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return H
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