# Source code for networkx.algorithms.coloring.equitable_coloring

```
# -*- coding: utf-8 -*-
# Copyright (C) 2018 by
# Utkarsh Upadhyay <musically.ut@gmail.com>
# All rights reserved.
# BSD license.
"""
Equitable coloring of graphs with bounded degree.
"""
import networkx as nx
from collections import defaultdict
__all__ = ['equitable_color']
def is_coloring(G, coloring):
"""Determine if the coloring is a valid coloring for the graph G."""
# Verify that the coloring is valid.
for (s, d) in G.edges:
if coloring[s] == coloring[d]:
return False
return True
def is_equitable(G, coloring, num_colors=None):
"""Determines if the coloring is valid and equitable for the graph G."""
if not is_coloring(G, coloring):
return False
# Verify whether it is equitable.
color_set_size = defaultdict(int)
for color in coloring.values():
color_set_size[color] += 1
if num_colors is not None:
for color in range(num_colors):
if color not in color_set_size:
# These colors do not have any vertices attached to them.
color_set_size[color] = 0
# If there are more than 2 distinct values, the coloring cannot be equitable
all_set_sizes = set(color_set_size.values())
if len(all_set_sizes) == 0 and num_colors is None: # Was an empty graph
return True
elif len(all_set_sizes) == 1:
return True
elif len(all_set_sizes) == 2:
a, b = list(all_set_sizes)
return abs(a - b) <= 1
else: # len(all_set_sizes) > 2:
return False
def make_C_from_F(F):
C = defaultdict(lambda: [])
for node, color in F.items():
C[color].append(node)
return C
def make_N_from_L_C(L, C):
nodes = L.keys()
colors = C.keys()
return {(node, color): sum(1 for v in L[node] if v in C[color])
for node in nodes for color in colors}
def make_H_from_C_N(C, N):
return {(c1, c2): sum(1 for node in C[c1] if N[(node, c2)] == 0)
for c1 in C.keys() for c2 in C.keys()}
def change_color(u, X, Y, N, H, F, C, L):
"""Change the color of 'u' from X to Y and update N, H, F, C."""
assert F[u] == X and X != Y
# Change the class of 'u' from X to Y
F[u] = Y
for k in C.keys():
# 'u' witnesses an edge from k -> Y instead of from k -> X now.
if N[u, k] == 0:
H[(X, k)] -= 1
H[(Y, k)] += 1
for v in L[u]:
# 'v' has lost a neighbor in X and gained one in Y
N[(v, X)] -= 1
N[(v, Y)] += 1
if N[(v, X)] == 0:
# 'v' witnesses F[v] -> X
H[(F[v], X)] += 1
if N[(v, Y)] == 1:
# 'v' no longer witnesses F[v] -> Y
H[(F[v], Y)] -= 1
C[X].remove(u)
C[Y].append(u)
def move_witnesses(src_color, dst_color, N, H, F, C, T_cal, L):
"""Move witness along a path from src_color to dst_color."""
X = src_color
while X != dst_color:
Y = T_cal[X]
# Move _any_ witness from X to Y = T_cal[X]
w = [x for x in C[X] if N[(x, Y)] == 0][0]
change_color(w, X, Y, N=N, H=H, F=F, C=C, L=L)
X = Y
def pad_graph(G, num_colors):
"""Add a disconnected complete clique K_p such that the number of nodes in
the graph becomes a multiple of `num_colors`.
Assumes that the graph's nodes are labelled using integers.
Returns the number of nodes with each color.
"""
n_ = len(G)
r = num_colors - 1
# Ensure that the number of nodes in G is a multiple of (r + 1)
s = n_ // (r + 1)
if n_ != s * (r + 1):
p = (r + 1) - n_ % (r + 1)
s += 1
# Complete graph K_p between (imaginary) nodes [n_, ... , n_ + p]
K = nx.relabel_nodes(nx.complete_graph(p),
{idx: idx + n_ for idx in range(p)})
G.add_edges_from(K.edges)
return s
def procedure_P(V_minus, V_plus, N, H, F, C, L, excluded_colors=None):
"""Procedure P as described in the paper."""
if excluded_colors is None:
excluded_colors = set()
A_cal = set()
T_cal = {}
R_cal = []
# BFS to determine A_cal, i.e. colors reachable from V-
reachable = [V_minus]
marked = set(reachable)
idx = 0
while idx < len(reachable):
pop = reachable[idx]
idx += 1
A_cal.add(pop)
R_cal.append(pop)
# TODO: Checking whether a color has been visited can be made faster by
# using a look-up table instead of testing for membership in a set by a
# logarithmic factor.
next_layer = []
for k in C.keys():
if H[(k, pop)] > 0 and \
k not in A_cal and \
k not in excluded_colors and \
k not in marked:
next_layer.append(k)
for dst in next_layer:
# Record that `dst` can reach `pop`
T_cal[dst] = pop
marked.update(next_layer)
reachable.extend(next_layer)
# Variables for the algorithm
b = (len(C) - len(A_cal))
if V_plus in A_cal:
# Easy case: V+ is in A_cal
# Move one node from V+ to V- using T_cal to find the parents.
move_witnesses(V_plus, V_minus, N=N, H=H, F=F, C=C, T_cal=T_cal, L=L)
else:
# If there is a solo edge, we can resolve the situation by
# moving witnesses from B to A, making G[A] equitable and then
# recursively balancing G[B - w] with a different V_minus and
# but the same V_plus.
A_0 = set()
A_cal_0 = set()
num_terminal_sets_found = 0
made_equitable = False
for W_1 in R_cal[::-1]:
for v in C[W_1]:
X = None
for U in C.keys():
if N[(v, U)] == 0 and U in A_cal and U != W_1:
X = U
# v does not witness an edge in H[A_cal]
if X is None:
continue
for U in C.keys():
# Note: Departing from the paper here.
if N[(v, U)] >= 1 and U not in A_cal:
X_prime = U
w = v
# Finding the solo neighbor of w in X_prime
y_candidates = [node for node in L[w]
if F[node] == X_prime and N[(node, W_1)] == 1]
if len(y_candidates) > 0:
y = y_candidates[0]
W = W_1
# Move w from W to X, now X has one extra node.
change_color(w, W, X, N=N, H=H, F=F, C=C, L=L)
# Move witness from X to V_minus, making the coloring
# equitable.
move_witnesses(src_color=X, dst_color=V_minus,
N=N, H=H, F=F, C=C, T_cal=T_cal, L=L)
# Move y from X_prime to W, making W the correct size.
change_color(y, X_prime, W, N=N, H=H, F=F, C=C, L=L)
# Then call the procedure on G[B - y]
procedure_P(V_minus=X_prime, V_plus=V_plus,
N=N, H=H, C=C, F=F, L=L,
excluded_colors=excluded_colors.union(A_cal))
made_equitable = True
break
if made_equitable:
break
else:
# No node in W_1 was found such that
# it had a solo-neighbor.
A_cal_0.add(W_1)
A_0.update(C[W_1])
num_terminal_sets_found += 1
if num_terminal_sets_found == b:
# Otherwise, construct the maximal independent set and find
# a pair of z_1, z_2 as in Case II.
# BFS to determine B_cal': the set of colors reachable from V+
B_cal_prime = set()
T_cal_prime = {}
reachable = [V_plus]
marked = set(reachable)
idx = 0
while idx < len(reachable):
pop = reachable[idx]
idx += 1
B_cal_prime.add(pop)
# No need to check for excluded_colors here because
# they only exclude colors from A_cal
next_layer = [k for k in C.keys()
if H[(pop, k)] > 0 and
k not in B_cal_prime and
k not in marked]
for dst in next_layer:
T_cal_prime[pop] = dst
marked.update(next_layer)
reachable.extend(next_layer)
# Construct the independent set of G[B']
I_set = set()
I_covered = set()
W_covering = {}
B_prime = [node for k in B_cal_prime for node in C[k]]
# Add the nodes in V_plus to I first.
for z in C[V_plus] + B_prime:
if z in I_covered or F[z] not in B_cal_prime:
continue
I_set.add(z)
I_covered.add(z)
I_covered.update([nbr for nbr in L[z]])
for w in L[z]:
if F[w] in A_cal_0 and N[(z, F[w])] == 1:
if w not in W_covering:
W_covering[w] = z
else:
# Found z1, z2 which have the same solo
# neighbor in some W
z_1 = W_covering[w]
# z_2 = z
Z = F[z_1]
W = F[w]
# shift nodes along W, V-
move_witnesses(W, V_minus,
N=N, H=H, F=F, C=C,
T_cal=T_cal, L=L)
# shift nodes along V+ to Z
move_witnesses(V_plus, Z,
N=N, H=H, F=F, C=C,
T_cal=T_cal_prime, L=L)
# change color of z_1 to W
change_color(z_1, Z, W,
N=N, H=H, F=F, C=C, L=L)
# change color of w to some color in B_cal
W_plus = [k for k in C.keys()
if N[(w, k)] == 0 and
k not in A_cal][0]
change_color(w, W, W_plus,
N=N, H=H, F=F, C=C, L=L)
# recurse with G[B \cup W*]
excluded_colors.update([
k for k in C.keys()
if k != W and k not in B_cal_prime
])
procedure_P(V_minus=W, V_plus=W_plus,
N=N, H=H, C=C, F=F, L=L,
excluded_colors=excluded_colors)
made_equitable = True
break
if made_equitable:
break
else:
assert False, "Must find a w which is the solo neighbor " \
"of two vertices in B_cal_prime."
if made_equitable:
break
[docs]def equitable_color(G, num_colors):
"""Provides equitable (r + 1)-coloring for nodes of G in O(r * n^2) time
if deg(G) <= r. The algorithm is described in [1]_.
Attempts to color a graph using r colors, where no neighbors of a node
can have same color as the node itself and the number of nodes with each
color differ by at most 1.
Parameters
----------
G : networkX graph
The nodes of this graph will be colored.
num_colors : number of colors to use
This number must be at least one more than the maximum degree of nodes
in the graph.
Returns
-------
A dictionary with keys representing nodes and values representing
corresponding coloring.
Examples
--------
>>> G = nx.cycle_graph(4)
>>> d = nx.coloring.equitable_color(G, num_colors=3)
>>> nx.algorithms.coloring.equitable_coloring.is_equitable(G, d)
True
Raises
------
NetworkXAlgorithmError
If the maximum degree of the graph ``G`` is greater than
``num_colors``.
References
----------
.. [1] Kierstead, H. A., Kostochka, A. V., Mydlarz, M., & Szemerédi, E.
(2010). A fast algorithm for equitable coloring. Combinatorica, 30(2),
217-224.
"""
# Map nodes to integers for simplicity later.
nodes_to_int = {}
int_to_nodes = {}
for idx, node in enumerate(G.nodes):
nodes_to_int[node] = idx
int_to_nodes[idx] = node
G = nx.relabel_nodes(G, nodes_to_int, copy=True)
# Basic graph statistics and sanity check.
if len(G.nodes) > 0:
r_ = max([G.degree(node) for node in G.nodes])
else:
r_ = 0
if r_ >= num_colors:
raise nx.NetworkXAlgorithmError(
'Graph has maximum degree {}, needs {} (> {}) colors for guaranteed coloring.'
.format(r_, r_ + 1, num_colors)
)
# Ensure that the number of nodes in G is a multiple of (r + 1)
pad_graph(G, num_colors)
# Starting the algorithm.
# L = {node: list(G.neighbors(node)) for node in G.nodes}
L_ = {node: [] for node in G.nodes}
# Arbitrary equitable allocation of colors to nodes.
F = {node: idx % num_colors for idx, node in enumerate(G.nodes)}
C = make_C_from_F(F)
# The neighborhood is empty initially.
N = make_N_from_L_C(L_, C)
# Currently all nodes witness all edges.
H = make_H_from_C_N(C, N)
# Start of algorithm.
edges_seen = set()
for u in sorted(G.nodes):
for v in sorted(G.neighbors(u)):
# Do not double count edges if (v, u) has already been seen.
if (v, u) in edges_seen:
continue
edges_seen.add((u, v))
L_[u].append(v)
L_[v].append(u)
N[(u, F[v])] += 1
N[(v, F[u])] += 1
if F[u] != F[v]:
# Were 'u' and 'v' witnesses for F[u] -> F[v] or F[v] -> F[u]?
if N[(u, F[v])] == 1:
H[F[u], F[v]] -= 1 # u cannot witness an edge between F[u], F[v]
if N[(v, F[u])] == 1:
H[F[v], F[u]] -= 1 # v cannot witness an edge between F[v], F[u]
if N[(u, F[u])] != 0:
# Find the first color where 'u' does not have any neighbors.
Y = [k for k in C.keys() if N[(u, k)] == 0][0]
X = F[u]
change_color(u, X, Y, N=N, H=H, F=F, C=C, L=L_)
# Procedure P
procedure_P(V_minus=X, V_plus=Y,
N=N, H=H, F=F, C=C, L=L_)
return {int_to_nodes[x]: F[x] for x in int_to_nodes}
```