158 lines
4.5 KiB
Python
158 lines
4.5 KiB
Python
"""
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Cuthill-McKee ordering of graph nodes to produce sparse matrices
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"""
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from collections import deque
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from operator import itemgetter
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import networkx as nx
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from ..utils import arbitrary_element
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__all__ = ["cuthill_mckee_ordering", "reverse_cuthill_mckee_ordering"]
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def cuthill_mckee_ordering(G, heuristic=None):
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"""Generate an ordering (permutation) of the graph nodes to make
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a sparse matrix.
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Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
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Parameters
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----------
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G : graph
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A NetworkX graph
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heuristic : function, optional
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Function to choose starting node for RCM algorithm. If None
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a node from a pseudo-peripheral pair is used. A user-defined function
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can be supplied that takes a graph object and returns a single node.
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Returns
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-------
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nodes : generator
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Generator of nodes in Cuthill-McKee ordering.
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Examples
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--------
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>>> from networkx.utils import cuthill_mckee_ordering
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>>> G = nx.path_graph(4)
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>>> rcm = list(cuthill_mckee_ordering(G))
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>>> A = nx.adjacency_matrix(G, nodelist=rcm)
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Smallest degree node as heuristic function:
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>>> def smallest_degree(G):
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... return min(G, key=G.degree)
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>>> rcm = list(cuthill_mckee_ordering(G, heuristic=smallest_degree))
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See Also
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--------
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reverse_cuthill_mckee_ordering
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Notes
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-----
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The optimal solution the bandwidth reduction is NP-complete [2]_.
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References
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----------
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.. [1] E. Cuthill and J. McKee.
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Reducing the bandwidth of sparse symmetric matrices,
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In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
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http://doi.acm.org/10.1145/800195.805928
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.. [2] Steven S. Skiena. 1997. The Algorithm Design Manual.
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Springer-Verlag New York, Inc., New York, NY, USA.
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"""
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for c in nx.connected_components(G):
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yield from connected_cuthill_mckee_ordering(G.subgraph(c), heuristic)
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def reverse_cuthill_mckee_ordering(G, heuristic=None):
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"""Generate an ordering (permutation) of the graph nodes to make
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a sparse matrix.
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Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)
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[1]_.
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Parameters
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----------
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G : graph
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A NetworkX graph
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heuristic : function, optional
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Function to choose starting node for RCM algorithm. If None
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a node from a pseudo-peripheral pair is used. A user-defined function
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can be supplied that takes a graph object and returns a single node.
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Returns
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-------
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nodes : generator
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Generator of nodes in reverse Cuthill-McKee ordering.
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Examples
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--------
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>>> from networkx.utils import reverse_cuthill_mckee_ordering
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>>> G = nx.path_graph(4)
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>>> rcm = list(reverse_cuthill_mckee_ordering(G))
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>>> A = nx.adjacency_matrix(G, nodelist=rcm)
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Smallest degree node as heuristic function:
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>>> def smallest_degree(G):
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... return min(G, key=G.degree)
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>>> rcm = list(reverse_cuthill_mckee_ordering(G, heuristic=smallest_degree))
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See Also
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--------
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cuthill_mckee_ordering
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Notes
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-----
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The optimal solution the bandwidth reduction is NP-complete [2]_.
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References
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----------
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.. [1] E. Cuthill and J. McKee.
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Reducing the bandwidth of sparse symmetric matrices,
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In Proc. 24th Nat. Conf. ACM, pages 157-72, 1969.
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http://doi.acm.org/10.1145/800195.805928
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.. [2] Steven S. Skiena. 1997. The Algorithm Design Manual.
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Springer-Verlag New York, Inc., New York, NY, USA.
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"""
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return reversed(list(cuthill_mckee_ordering(G, heuristic=heuristic)))
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def connected_cuthill_mckee_ordering(G, heuristic=None):
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# the cuthill mckee algorithm for connected graphs
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if heuristic is None:
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start = pseudo_peripheral_node(G)
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else:
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start = heuristic(G)
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visited = {start}
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queue = deque([start])
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while queue:
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parent = queue.popleft()
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yield parent
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nd = sorted(list(G.degree(set(G[parent]) - visited)), key=itemgetter(1))
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children = [n for n, d in nd]
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visited.update(children)
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queue.extend(children)
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def pseudo_peripheral_node(G):
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# helper for cuthill-mckee to find a node in a "pseudo peripheral pair"
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# to use as good starting node
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u = arbitrary_element(G)
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lp = 0
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v = u
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while True:
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spl = dict(nx.shortest_path_length(G, v))
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l = max(spl.values())
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if l <= lp:
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break
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lp = l
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farthest = (n for n, dist in spl.items() if dist == l)
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v, deg = min(G.degree(farthest), key=itemgetter(1))
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return v
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