# Source code for networkx.algorithms.clique

"""
=======
Cliques
=======

Find and manipulate cliques of graphs.

Note that finding the largest clique of a graph has been
shown to be an NP-complete problem; the algorithms here
could take a long time to run.

http://en.wikipedia.org/wiki/Clique_problem
"""
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
from collections import deque
from itertools import chain, islice
try:
from itertools import ifilter as filter
except ImportError:
pass
import networkx
from networkx.utils.decorators import *
__author__ = """Dan Schult (dschult@colgate.edu)"""
__all__ = ['find_cliques', 'find_cliques_recursive', 'make_max_clique_graph',
'make_clique_bipartite' ,'graph_clique_number',
'graph_number_of_cliques', 'node_clique_number',
'number_of_cliques', 'cliques_containing_node',
'project_down', 'project_up', 'enumerate_all_cliques']

@not_implemented_for('directed')
[docs]def enumerate_all_cliques(G):
"""Returns all cliques in an undirected graph.

This method returns cliques of size (cardinality)
k = 1, 2, 3, ..., maxDegree - 1.

Where maxDegree is the maximal degree of any node in the graph.

Parameters
----------
G: undirected graph

Returns
-------
generator of lists: generator of list for each clique.

Notes
-----
To obtain a list of all cliques, use
:samp:list(enumerate_all_cliques(G)).

and adapted to output all cliques discovered.

This algorithm is not applicable on directed graphs.

This algorithm ignores self-loops and parallel edges as
clique is not conventionally defined with such edges.

There are often many cliques in graphs.
This algorithm however, hopefully, does not run out of memory
since it only keeps candidate sublists in memory and
continuously removes exhausted sublists.

References
----------
..  Yun Zhang, Abu-Khzam, F.N., Baldwin, N.E., Chesler, E.J.,
Langston, M.A., Samatova, N.F.,
Genome-Scale Computational Approaches to Memory-Intensive
Applications in Systems Biology.
Supercomputing, 2005. Proceedings of the ACM/IEEE SC 2005
Conference, pp. 12, 12-18 Nov. 2005.
doi: 10.1109/SC.2005.29.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1559964&isnumber=33129
"""
index = {}
nbrs = {}
for u in G:
index[u] = len(index)
# Neighbors of u that appear after u in the iteration order of G.
nbrs[u] = {v for v in G[u] if v not in index}

queue = deque(([u], sorted(nbrs[u], key=index.__getitem__)) for u in G)
# Loop invariants:
# 1. len(base) is nondecreasing.
# 2. (base + cnbrs) is sorted with respect to the iteration order of G.
# 3. cnbrs is a set of common neighbors of nodes in base.
while queue:
base, cnbrs = map(list, queue.popleft())
yield base
for i, u in enumerate(cnbrs):
# Use generators to reduce memory consumption.
queue.append((chain(base, [u]),
filter(nbrs[u].__contains__,
islice(cnbrs, i + 1, None))))

@not_implemented_for('directed')
[docs]def find_cliques(G):
"""Search for all maximal cliques in a graph.

Maximal cliques are the largest complete subgraph containing
a given node.  The largest maximal clique is sometimes called
the maximum clique.

Returns
-------
generator of lists: genetor of member list for each maximal clique

--------
find_cliques_recursive :
A recursive version of the same algorithm

Notes
-----
To obtain a list of cliques, use list(find_cliques(G)).

as adapted by Tomita, Tanaka and Takahashi (2006) _
and discussed in Cazals and Karande (2008) _.
The method essentially unrolls the recursion used in
the references to avoid issues of recursion stack depth.

This algorithm is not suitable for directed graphs.

This algorithm ignores self-loops and parallel edges as
clique is not conventionally defined with such edges.

There are often many cliques in graphs.  This algorithm can
run out of memory for large graphs.

References
----------
..  Bron, C. and Kerbosch, J. 1973.
Algorithm 457: finding all cliques of an undirected graph.
Commun. ACM 16, 9 (Sep. 1973), 575-577.
http://portal.acm.org/citation.cfm?doid=362342.362367

..  Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi,
The worst-case time complexity for generating all maximal
cliques and computational experiments,
Theoretical Computer Science, Volume 363, Issue 1,
Computing and Combinatorics,
10th Annual International Conference on
Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28-42
http://dx.doi.org/10.1016/j.tcs.2006.06.015

..  F. Cazals, C. Karande,
A note on the problem of reporting maximal cliques,
Theoretical Computer Science,
Volume 407, Issues 1-3, 6 November 2008, Pages 564-568,
http://dx.doi.org/10.1016/j.tcs.2008.05.010
"""
if len(G) == 0:
return

adj = {u: {v for v in G[u] if v != u} for u in G}
Q = [None]

subg = set(G)
cand = set(G)
u = max(subg, key=lambda u: len(cand & adj[u]))
stack = []

try:
while True:
if ext_u:
q = ext_u.pop()
cand.remove(q)
Q[-1] = q
if not subg_q:
yield Q[:]
else:
if cand_q:
stack.append((subg, cand, ext_u))
Q.append(None)
subg = subg_q
cand = cand_q
u = max(subg, key=lambda u: len(cand & adj[u]))
else:
Q.pop()
subg, cand, ext_u = stack.pop()
except IndexError:
pass

def find_cliques_recursive(G):
"""Recursive search for all maximal cliques in a graph.

Maximal cliques are the largest complete subgraph containing
a given point.  The largest maximal clique is sometimes called
the maximum clique.

Returns
-------
list of lists: list of members in each maximal clique

--------
find_cliques : An nonrecursive version of the same algorithm

Notes
-----
as adapted by Tomita, Tanaka and Takahashi (2006) _
and discussed in Cazals and Karande (2008) _.

This implementation returns a list of lists each of
which contains the members of a maximal clique.

This algorithm ignores self-loops and parallel edges as
clique is not conventionally defined with such edges.

References
----------
..  Bron, C. and Kerbosch, J. 1973.
Algorithm 457: finding all cliques of an undirected graph.
Commun. ACM 16, 9 (Sep. 1973), 575-577.
http://portal.acm.org/citation.cfm?doid=362342.362367

..  Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi,
The worst-case time complexity for generating all maximal
cliques and computational experiments,
Theoretical Computer Science, Volume 363, Issue 1,
Computing and Combinatorics,
10th Annual International Conference on
Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28-42
http://dx.doi.org/10.1016/j.tcs.2006.06.015

..  F. Cazals, C. Karande,
A note on the problem of reporting maximal cliques,
Theoretical Computer Science,
Volume 407, Issues 1-3, 6 November 2008, Pages 564-568,
http://dx.doi.org/10.1016/j.tcs.2008.05.010
"""
if len(G) == 0:
return iter([])

adj = {u: {v for v in G[u] if v != u} for u in G}
Q = []

def expand(subg, cand):
u = max(subg, key=lambda u: len(cand & adj[u]))
for q in cand - adj[u]:
cand.remove(q)
Q.append(q)
if not subg_q:
yield Q[:]
else:
if cand_q:
for clique in expand(subg_q, cand_q):
yield clique
Q.pop()

return expand(set(G), set(G))

[docs]def make_max_clique_graph(G,create_using=None,name=None):
""" Create the maximal clique graph of a graph.

Finds the maximal cliques and treats these as nodes.
The nodes are connected if they have common members in
the original graph.  Theory has done a lot with clique
graphs, but I haven't seen much on maximal clique graphs.

Notes
-----
This should be the same as make_clique_bipartite followed
by project_up, but it saves all the intermediate steps.
"""
cliq=list(map(set,find_cliques(G)))
if create_using:
B=create_using
B.clear()
else:
B=networkx.Graph()
if name is not None:
B.name=name

for i,cl in enumerate(cliq):
for j,other_cl in enumerate(cliq[:i]):
# if not cl.isdisjoint(other_cl): #Requires 2.6
intersect=cl & other_cl
if intersect:     # Not empty
return B

[docs]def make_clique_bipartite(G,fpos=None,create_using=None,name=None):
"""Create a bipartite clique graph from a graph G.

Nodes of G are retained as the "bottom nodes" of B and
cliques of G become "top nodes" of B.
Edges are present if a bottom node belongs to the clique
represented by the top node.

Returns a Graph with additional attribute dict B.node_type
which is keyed by nodes to "Bottom" or "Top" appropriately.

if fpos is not None, a second additional attribute dict B.pos
is created to hold the position tuple of each node for viewing
the bipartite graph.
"""
cliq=list(find_cliques(G))
if create_using:
B=create_using
B.clear()
else:
B=networkx.Graph()
if name is not None:
B.name=name

B.node_type={}   # New Attribute for B
for n in B:
B.node_type[n]="Bottom"

if fpos:
B.pos={}     # New Attribute for B
delta_cpos=1./len(cliq)
delta_ppos=1./G.order()
cpos=0.
ppos=0.
for i,cl in enumerate(cliq):
name= -i-1   # Top nodes get negative names
B.node_type[name]="Top"
if fpos:
if name not in B.pos:
B.pos[name]=(0.2,cpos)
cpos +=delta_cpos
for v in cl:
if fpos is not None:
if v not in B.pos:
B.pos[v]=(0.8,ppos)
ppos +=delta_ppos
return B

def project_down(B,create_using=None,name=None):
"""Project a bipartite graph B down onto its "bottom nodes".

The nodes retain their names and are connected if they
share a common top node in the bipartite graph.

Returns a Graph.
"""
if create_using:
G=create_using
G.clear()
else:
G=networkx.Graph()
if name is not None:
G.name=name

if B.node_type[v]=="Bottom":
for cv in Bvnbrs:
G.add_edges_from([(v,u) for u in B[cv] if u!=v])
return G

def project_up(B,create_using=None,name=None):
"""Project a bipartite graph B down onto its "bottom nodes".

The nodes retain their names and are connected if they
share a common Bottom Node in the Bipartite Graph.

Returns a Graph.
"""
if create_using:
G=create_using
G.clear()
else:
G=networkx.Graph()
if name is not None:
G.name=name

if B.node_type[v]=="Top":
vname= -v   #Change sign of name for Top Nodes
for cv in Bvnbrs:
# Note: -u changes the name (not Top node anymore)
G.add_edges_from([(vname,-u) for u in B[cv] if u!=v])
return G

[docs]def graph_clique_number(G,cliques=None):
"""Return the clique number (size of the largest clique) for G.

An optional list of cliques can be input if already computed.
"""
if cliques is None:
cliques=find_cliques(G)
return   max( [len(c) for c in cliques] )

[docs]def graph_number_of_cliques(G,cliques=None):
"""Returns the number of maximal cliques in G.

An optional list of cliques can be input if already computed.
"""
if cliques is None:
cliques=list(find_cliques(G))
return   len(cliques)

[docs]def node_clique_number(G,nodes=None,cliques=None):
""" Returns the size of the largest maximal clique containing
each given node.

Returns a single or list depending on input nodes.
Optional list of cliques can be input if already computed.
"""
if cliques is None:
if nodes is not None:
# Use ego_graph to decrease size of graph
if isinstance(nodes,list):
d={}
for n in nodes:
H=networkx.ego_graph(G,n)
d[n]=max( (len(c) for c in find_cliques(H)) )
else:
H=networkx.ego_graph(G,nodes)
d=max( (len(c) for c in find_cliques(H)) )
return d
# nodes is None--find all cliques
cliques=list(find_cliques(G))

if nodes is None:
nodes=G.nodes()   # none, get entire graph

if not isinstance(nodes, list):   # check for a list
v=nodes
# assume it is a single value
d=max([len(c) for c in cliques if v in c])
else:
d={}
for v in nodes:
d[v]=max([len(c) for c in cliques if v in c])
return d

# if nodes is None:                 # none, use entire graph
#     nodes=G.nodes()
# elif  not isinstance(nodes, list):    # check for a list
#     nodes=[nodes]             # assume it is a single value

# if cliques is None:
#     cliques=list(find_cliques(G))
# d={}
# for v in nodes:
#     d[v]=max([len(c) for c in cliques if v in c])

# if nodes in G:
#     return d[v] #return single value
# return d

[docs]def number_of_cliques(G,nodes=None,cliques=None):
"""Returns the number of maximal cliques for each node.

Returns a single or list depending on input nodes.
Optional list of cliques can be input if already computed.
"""
if cliques is None:
cliques=list(find_cliques(G))

if nodes is None:
nodes=G.nodes()   # none, get entire graph

if not isinstance(nodes, list):   # check for a list
v=nodes
# assume it is a single value
numcliq=len([1 for c in cliques if v in c])
else:
numcliq={}
for v in nodes:
numcliq[v]=len([1 for c in cliques if v in c])
return numcliq

[docs]def cliques_containing_node(G,nodes=None,cliques=None):
"""Returns a list of cliques containing the given node.

Returns a single list or list of lists depending on input nodes.
Optional list of cliques can be input if already computed.
"""
if cliques is None:
cliques=list(find_cliques(G))

if nodes is None:
nodes=G.nodes()   # none, get entire graph

if not isinstance(nodes, list):   # check for a list
v=nodes
# assume it is a single value
vcliques=[c for c in cliques if v in c]
else:
vcliques={}
for v in nodes:
vcliques[v]=[c for c in cliques if v in c]
return vcliques