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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():

    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


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)})

    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


        # 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:

        for dst in next_layer:
            # Record that `dst` can reach `pop`
            T_cal[dst] = pop


    # 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)
        # 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:

                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,
                            made_equitable = True

                if made_equitable:
                # No node in W_1 was found such that
                # it had a solo-neighbor.
                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


                    # 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


                # 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:

                    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
                                # 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*]
                                    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,

                                made_equitable = True

                    if made_equitable:
                    assert False, "Must find a w which is the solo neighbor " \
                                  "of two vertices in B_cal_prime."

            if made_equitable:

[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., & Szemer├ędi, 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}