# -*- coding: utf-8 -*-
"""
Strongly connected components.
"""
__authors__ = "\n".join(['Eben Kenah',
'Aric Hagberg (hagberg@lanl.gov)'
'Christopher Ellison'])
# Copyright (C) 2004-2010 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
__all__ = ['number_strongly_connected_components',
'strongly_connected_components',
'strongly_connected_component_subgraphs',
'is_strongly_connected',
'strongly_connected_components_recursive',
'kosaraju_strongly_connected_components',
'condensation',
]
import networkx as nx
def strongly_connected_components(G):
"""Return nodes in strongly connected components of graph.
Parameters
----------
G : NetworkX Graph
An directed graph.
Returns
-------
comp : list of lists
A list of nodes for each component of G.
The list is ordered from largest connected component to smallest.
See Also
--------
connected_components
Notes
-----
Uses Tarjan's algorithm with Nuutila's modifications.
Nonrecursive version of algorithm.
References
----------
.. [1] Depth-first search and linear graph algorithms, R. Tarjan
SIAM Journal of Computing 1(2):146-160, (1972).
.. [2] On finding the strongly connected components in a directed graph.
E. Nuutila and E. Soisalon-Soinen
Information Processing Letters 49(1): 9-14, (1994)..
"""
preorder={}
lowlink={}
scc_found={}
scc_queue = []
scc_list=[]
i=0 # Preorder counter
for source in G:
if source not in scc_found:
queue=[source]
while queue:
v=queue[-1]
if v not in preorder:
i=i+1
preorder[v]=i
done=1
v_nbrs=G[v]
for w in v_nbrs:
if w not in preorder:
queue.append(w)
done=0
break
if done==1:
lowlink[v]=preorder[v]
for w in v_nbrs:
if w not in scc_found:
if preorder[w]>preorder[v]:
lowlink[v]=min([lowlink[v],lowlink[w]])
else:
lowlink[v]=min([lowlink[v],preorder[w]])
queue.pop()
if lowlink[v]==preorder[v]:
scc_found[v]=True
scc=[v]
while scc_queue and preorder[scc_queue[-1]]>preorder[v]:
k=scc_queue.pop()
scc_found[k]=True
scc.append(k)
scc_list.append(scc)
else:
scc_queue.append(v)
scc_list.sort(key=len,reverse=True)
return scc_list
def kosaraju_strongly_connected_components(G,source=None):
"""Return nodes in strongly connected components of graph.
Parameters
----------
G : NetworkX Graph
An directed graph.
Returns
-------
comp : list of lists
A list of nodes for each component of G.
The list is ordered from largest connected component to smallest.
See Also
--------
connected_components
Notes
-----
Uses Kosaraju's algorithm.
"""
components=[]
post=nx.dfs_postorder(G,source=source,reverse_graph=True)
seen={}
while post:
r=post.pop()
if r in seen:
continue
c=nx.dfs_preorder(G,r)
new=[v for v in c if v not in seen]
seen.update([(u,True) for u in new])
components.append(new)
components.sort(key=len,reverse=True)
return components
def strongly_connected_components_recursive(G):
"""Return nodes in strongly connected components of graph.
Recursive version of algorithm.
Parameters
----------
G : NetworkX Graph
An directed graph.
Returns
-------
comp : list of lists
A list of nodes for each component of G.
The list is ordered from largest connected component to smallest.
See Also
--------
connected_components
Notes
-----
Uses Tarjan's algorithm with Nuutila's modifications.
References
----------
.. [1] Depth-first search and linear graph algorithms, R. Tarjan
SIAM Journal of Computing 1(2):146-160, (1972).
.. [2] On finding the strongly connected components in a directed graph.
E. Nuutila and E. Soisalon-Soinen
Information Processing Letters 49(1): 9-14, (1994)..
"""
def visit(v,cnt):
root[v]=cnt
visited[v]=cnt
cnt+=1
stack.append(v)
for w in G[v]:
if w not in visited: visit(w,cnt)
if w not in component:
root[v]=min(root[v],root[w])
if root[v]==visited[v]:
component[v]=root[v]
tmpc=[v] # hold nodes in this component
while stack[-1]!=v:
w=stack.pop()
component[w]=root[v]
tmpc.append(w)
stack.remove(v)
scc.append(tmpc) # add to scc list
scc=[]
visited={}
component={}
root={}
cnt=0
stack=[]
for source in G:
if source not in visited:
visit(source,cnt)
scc.sort(key=len,reverse=True)
return scc
def strongly_connected_component_subgraphs(G):
"""Return strongly connected components as subgraphs.
Parameters
----------
G : NetworkX Graph
A graph.
Returns
-------
glist : list
A list of graphs, one for each strongly connected component of G.
See Also
--------
connected_component_subgraphs
Notes
-----
The list is ordered from largest connected component to smallest.
For undirected graphs only.
"""
cc=strongly_connected_components(G)
graph_list=[]
for c in cc:
graph_list.append(G.subgraph(c))
return graph_list
def number_strongly_connected_components(G):
"""Return number of strongly connected components in graph.
Parameters
----------
G : NetworkX graph
A directed graph.
Returns
-------
n : integer
Number of strongly connected components
See Also
--------
connected_components
Notes
-----
For directed graphs only.
"""
return len(strongly_connected_components(G))
def is_strongly_connected(G):
"""Test directed graph for strong connectivity.
Parameters
----------
G : NetworkX Graph
A directed graph.
Returns
-------
connected : bool
True if the graph is strongly connected, False otherwise.
See Also
--------
strongly_connected_components
Notes
-----
For directed graphs only.
"""
if not G.is_directed():
raise nx.NetworkXError,\
"""Not allowed for undirected graph G.
See is_connected() for connectivity test."""
if len(G)==0:
raise nx.NetworkXPointlessConcept(
"""Connectivity is undefined for the null graph.""")
return len(strongly_connected_components(G)[0])==len(G)
def condensation(G):
"""Returns the condensation of G.
The condensation of G is the graph with each of the strongly connected
components contracted into a single node.
Parameters
----------
G : NetworkX Graph
A directed graph.
Returns
-------
cG : NetworkX DiGraph
The condensation of G.
Notes
-----
After contracting all strongly connected components to a single node,
the resulting graph is a directed acyclic graph.
"""
scc = strongly_connected_components(G)
mapping = dict([(n,tuple(sorted(c))) for c in scc for n in c])
cG = nx.DiGraph()
for u in mapping:
cG.add_node(mapping[u])
for _,v,d in G.edges_iter(u, data=True):
if v not in mapping[u]:
cG.add_edge(mapping[u], mapping[v])
return cG
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