Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.generators.hybrid

"""
Hybrid 

"""
__author__ = """Aric Hagberg (hagberg@lanl.gov)\nDan Schult (dschult@colgate.edu)"""
#    Copyright (C) 2004-2008 by 
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

_all__ = ['kl_connected_subgraph', 'is_kl_connected']

import copy
import networkx as nx

[docs]def kl_connected_subgraph(G,k,l,low_memory=False,same_as_graph=False): """ Returns the maximum locally (k,l) connected subgraph of G. (k,l)-connected subgraphs are presented by Fan Chung and Li in "The Small World Phenomenon in hybrid power law graphs" to appear in "Complex Networks" (Ed. E. Ben-Naim) Lecture Notes in Physics, Springer (2004) low_memory=True then use a slightly slower, but lower memory version same_as_graph=True then return a tuple with subgraph and pflag for if G is kl-connected """ H=copy.deepcopy(G) # subgraph we construct by removing from G graphOK=True deleted_some=True # hack to start off the while loop while deleted_some: deleted_some=False for edge in H.edges(): (u,v)=edge ### Get copy of graph needed for this search if low_memory: verts=set([u,v]) for i in range(k): [verts.update(G.neighbors(w)) for w in verts.copy()] G2=G.subgraph(list(verts)) else: G2=copy.deepcopy(G) ### path=[u,v] cnt=0 accept=0 while path: cnt += 1 # Found a path if cnt>=l: accept=1 break # record edges along this graph prev=u for w in path: if prev!=w: G2.remove_edge(prev,w) prev=w # path=shortest_path(G2,u,v,k) # ??? should "Cutoff" be k+1? try: path=nx.shortest_path(G2,u,v) # ??? should "Cutoff" be k+1? except nx.NetworkXNoPath: path = False # No Other Paths if accept==0: H.remove_edge(u,v) deleted_some=True if graphOK: graphOK=False # We looked through all edges and removed none of them. # So, H is the maximal (k,l)-connected subgraph of G if same_as_graph: return (H,graphOK) return H
[docs]def is_kl_connected(G,k,l,low_memory=False): """Returns True if G is kl connected.""" graphOK=True for edge in G.edges(): (u,v)=edge ### Get copy of graph needed for this search if low_memory: verts=set([u,v]) for i in range(k): [verts.update(G.neighbors(w)) for w in verts.copy()] G2=G.subgraph(verts) else: G2=copy.deepcopy(G) ### path=[u,v] cnt=0 accept=0 while path: cnt += 1 # Found a path if cnt>=l: accept=1 break # record edges along this graph prev=u for w in path: if w!=prev: G2.remove_edge(prev,w) prev=w # path=shortest_path(G2,u,v,k) # ??? should "Cutoff" be k+1? try: path=nx.shortest_path(G2,u,v) # ??? should "Cutoff" be k+1? except nx.NetworkXNoPath: path = False # No Other Paths if accept==0: graphOK=False break # return status return graphOK