Graph colouring
A Graph is a collection of nodes
(or vertices), connected by edges (or not).
Nodes directly connected by edges are called neighbours.
Graph colouring is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.
In our representation of graphs, nodes are numbered and edges are represented by the two node numbers connected by the edge separated by a dash. Edges define the nodes being connected. Only unconnected nodes need a separate description.
For example,
0-1 1-2 2-0 3
Describes the following graph. Note that node 3 has no neighbours
- Example graph
+---+ | 3 | +---+ +-------------------+ | | +---+ +---+ +---+ | 0 | --- | 1 | --- | 2 | +---+ +---+ +---+
A useful internal datastructure for a graph and for later graph algorithms is as a mapping between each node and the set/list of its neighbours.
In the above example:
0 maps-to 1 and 2 1 maps to 2 and 0 2 maps-to 1 and 0 3 maps-to <nothing>
- Graph colouring task
Colour the vertices of a given graph so that no edge is between verticies of the same colour.
- Integers may be used to denote different colours.
- Algorithm should do better than just assigning each vertex a separate colour. The idea is to minimise the number of colours used, although no algorithm short of exhaustive search for the minimum is known at present, (and exhaustive search is not a requirement).
- Show for each edge, the colours assigned on each vertex.
- Show the total number of nodes, edges, and colours used for each graph.
- Use the following graphs
- Ex1
0-1 1-2 2-0 3
+---+ | 3 | +---+ +-------------------+ | | +---+ +---+ +---+ | 0 | --- | 1 | --- | 2 | +---+ +---+ +---+
- Ex2
The wp articles left-side graph
1-6 1-7 1-8 2-5 2-7 2-8 3-5 3-6 3-8 4-5 4-6 4-7
+----------------------------------+
| |
| +---+ |
| +-----------------| 3 | ------+----+
| | +---+ | |
| | | | |
| | | | |
| | | | |
| +---+ +---+ +---+ +---+ |
| | 8 | --- | 1 | --- | 6 | --- | 4 | |
| +---+ +---+ +---+ +---+ |
| | | | |
| | | | |
| | | | |
| | +---+ +---+ +---+ |
+----+------ | 7 | --- | 2 | --- | 5 | -+
| +---+ +---+ +---+
| |
+-------------------+
- Ex3
The wp articles right-side graph which is the same graph as Ex2, but with different node orderings and namings.
1-4 1-6 1-8 3-2 3-6 3-8 5-2 5-4 5-8 7-2 7-4 7-6
+----------------------------------+
| |
| +---+ |
| +-----------------| 5 | ------+----+
| | +---+ | |
| | | | |
| | | | |
| | | | |
| +---+ +---+ +---+ +---+ |
| | 8 | --- | 1 | --- | 4 | --- | 7 | |
| +---+ +---+ +---+ +---+ |
| | | | |
| | | | |
| | | | |
| | +---+ +---+ +---+ |
+----+------ | 6 | --- | 3 | --- | 2 | -+
| +---+ +---+ +---+
| |
+-------------------+
- Ex4
This is the same graph, node naming, and edge order as Ex2 except some of the edges x-y are flipped to y-x. This might alter the node order used in the greedy algorithm leading to differing numbers of colours.
1-6 7-1 8-1 5-2 2-7 2-8 3-5 6-3 3-8 4-5 4-6 4-7
+-------------------------------------------------+
| |
| |
+-------------------+---------+ |
| | | |
+---+ +---+ +---+ +---+ +---+ +---+ +---+ +---+
| 4 | --- | 5 | --- | 2 | --- | 7 | --- | 1 | --- | 6 | --- | 3 | --- | 8 |
+---+ +---+ +---+ +---+ +---+ +---+ +---+ +---+
| | | | | |
+---------+-----------------------------+---------+ | |
| | | |
| | | |
+-----------------------------+-------------------+ |
| |
| |
+-----------------------------+
- References
- Greedy coloring Wikipedia.
- Graph Coloring : Greedy Algorithm & Welsh Powell Algorithm by Priyank Jain.
Go
As mentioned in the task description, there is no known efficient algorithm which can guarantee that a minimum number of colors is used for a given graph. The following uses both the so-called 'greedy' algorithm (as described here) and the Welsh-Powell algorithm (as described here), suitably adjusted to the needs of this task.
The results are exactly the same for both algorithms. Whilst one would normally expect Welsh-Powell to give better results overall, the last three examples are not well suited to it as each node has exactly the same number of neighbors i.e. the valences are equal.
The results agree with the Python entry for examples 1 and 2 but, for example 3, Python gives 2 colors compared to my 4 and, for example 4, Python gives 3 colors compared to my 2. <lang go>package main
import (
"fmt" "sort"
)
type graph struct {
nn int // number of nodes st int // node numbering starts from nbr [][]int // neighbor list for each node
}
type nodeval struct {
n int // number of node v int // valence of node i.e. number of neighbors
}
func contains(s []int, n int) bool {
for _, e := range s {
if e == n {
return true
}
}
return false
}
func newGraph(nn, st int) graph {
nbr := make([][]int, nn)
return graph{nn, st, nbr}
}
// Note that this creates a single 'virtual' edge for an isolated node. func (g graph) addEdge(n1, n2 int) {
n1, n2 = n1-g.st, n2-g.st // adjust to starting node number
g.nbr[n1] = append(g.nbr[n1], n2)
if n1 != n2 {
g.nbr[n2] = append(g.nbr[n2], n1)
}
}
// Uses 'greedy' algorithm. func (g graph) greedyColoring() []int {
// create a slice with a color for each node, starting with color 0
cols := make([]int, g.nn) // all zero by default including the first node
for i := 1; i < g.nn; i++ {
cols[i] = -1 // mark all nodes after the first as having no color assigned (-1)
}
// create a bool slice to keep track of which colors are available
available := make([]bool, g.nn) // all false by default
// assign colors to all nodes after the first
for i := 1; i < g.nn; i++ {
// iterate through neighbors and mark their colors as available
for _, j := range g.nbr[i] {
if cols[j] != -1 {
available[cols[j]] = true
}
}
// find the first available color
c := 0
for ; c < g.nn; c++ {
if !available[c] {
break
}
}
cols[i] = c // assign it to the current node
// reset the neighbors' colors to unavailable
// before the next iteration
for _, j := range g.nbr[i] {
if cols[j] != -1 {
available[cols[j]] = false
}
}
}
return cols
}
// Uses Welsh-Powell algorithm. func (g graph) wpColoring() []int {
// create nodeval for each node
nvs := make([]nodeval, g.nn)
for i := 0; i < g.nn; i++ {
v := len(g.nbr[i])
if v == 1 && g.nbr[i][0] == i { // isolated node
v = 0
}
nvs[i] = nodeval{i, v}
}
// sort the nodevals in descending order by valence
sort.Slice(nvs, func(i, j int) bool {
return nvs[i].v > nvs[j].v
})
// create colors slice with entries for each node
cols := make([]int, g.nn)
for i := range cols {
cols[i] = -1 // set all nodes to no color (-1) initially
}
currCol := 0 // start with color 0
for f := 0; f < g.nn-1; f++ {
h := nvs[f].n
if cols[h] != -1 { // already assigned a color
continue
}
cols[h] = currCol
// assign same color to all subsequent uncolored nodes which are
// not connected to a previous colored one
outer:
for i := f + 1; i < g.nn; i++ {
j := nvs[i].n
if cols[j] != -1 { // already colored
continue
}
for k := f; k < i; k++ {
l := nvs[k].n
if cols[l] == -1 { // not yet colored
continue
}
if contains(g.nbr[j], l) {
continue outer // node j is connected to an earlier colored node
}
}
cols[j] = currCol
}
currCol++
}
return cols
}
func main() {
fns := [](func(graph) []int){graph.greedyColoring, graph.wpColoring}
titles := []string{"'Greedy'", "Welsh-Powell"}
nns := []int{4, 8, 8, 8}
starts := []int{0, 1, 1, 1}
edges1 := [][2]int{{0, 1}, {1, 2}, {2, 0}, {3, 3}}
edges2 := [][2]int{{1, 6}, {1, 7}, {1, 8}, {2, 5}, {2, 7}, {2, 8},
{3, 5}, {3, 6}, {3, 8}, {4, 5}, {4, 6}, {4, 7}}
edges3 := [][2]int{{1, 4}, {1, 6}, {1, 8}, {3, 2}, {3, 6}, {3, 8},
{5, 2}, {5, 4}, {5, 8}, {7, 2}, {7, 4}, {7, 6}}
edges4 := [][2]int{{1, 6}, {7, 1}, {8, 1}, {5, 2}, {2, 7}, {2, 8},
{3, 5}, {6, 3}, {3, 8}, {4, 5}, {4, 6}, {4, 7}}
for j, fn := range fns {
fmt.Println("Using the", titles[j], "algorithm:\n")
for i, edges := range [][][2]int{edges1, edges2, edges3, edges4} {
fmt.Println(" Example", i+1)
g := newGraph(nns[i], starts[i])
for _, e := range edges {
g.addEdge(e[0], e[1])
}
cols := fn(g)
ecount := 0 // counts edges
for _, e := range edges {
if e[0] != e[1] {
fmt.Printf(" Edge %d-%d -> Color %d, %d\n", e[0], e[1],
cols[e[0]-g.st], cols[e[1]-g.st])
ecount++
} else {
fmt.Printf(" Node %d -> Color %d\n", e[0], cols[e[0]-g.st])
}
}
maxCol := 0 // maximum color number used
for _, col := range cols {
if col > maxCol {
maxCol = col
}
}
fmt.Println(" Number of nodes :", nns[i])
fmt.Println(" Number of edges :", ecount)
fmt.Println(" Number of colors :", maxCol+1)
fmt.Println()
}
}
}</lang>
- Output:
Using the 'Greedy' algorithm:
Example 1
Edge 0-1 -> Color 0, 1
Edge 1-2 -> Color 1, 2
Edge 2-0 -> Color 2, 0
Node 3 -> Color 0
Number of nodes : 4
Number of edges : 3
Number of colors : 3
Example 2
Edge 1-6 -> Color 0, 1
Edge 1-7 -> Color 0, 1
Edge 1-8 -> Color 0, 1
Edge 2-5 -> Color 0, 1
Edge 2-7 -> Color 0, 1
Edge 2-8 -> Color 0, 1
Edge 3-5 -> Color 0, 1
Edge 3-6 -> Color 0, 1
Edge 3-8 -> Color 0, 1
Edge 4-5 -> Color 0, 1
Edge 4-6 -> Color 0, 1
Edge 4-7 -> Color 0, 1
Number of nodes : 8
Number of edges : 12
Number of colors : 2
Example 3
Edge 1-4 -> Color 0, 1
Edge 1-6 -> Color 0, 2
Edge 1-8 -> Color 0, 3
Edge 3-2 -> Color 1, 0
Edge 3-6 -> Color 1, 2
Edge 3-8 -> Color 1, 3
Edge 5-2 -> Color 2, 0
Edge 5-4 -> Color 2, 1
Edge 5-8 -> Color 2, 3
Edge 7-2 -> Color 3, 0
Edge 7-4 -> Color 3, 1
Edge 7-6 -> Color 3, 2
Number of nodes : 8
Number of edges : 12
Number of colors : 4
Example 4
Edge 1-6 -> Color 0, 1
Edge 7-1 -> Color 1, 0
Edge 8-1 -> Color 1, 0
Edge 5-2 -> Color 1, 0
Edge 2-7 -> Color 0, 1
Edge 2-8 -> Color 0, 1
Edge 3-5 -> Color 0, 1
Edge 6-3 -> Color 1, 0
Edge 3-8 -> Color 0, 1
Edge 4-5 -> Color 0, 1
Edge 4-6 -> Color 0, 1
Edge 4-7 -> Color 0, 1
Number of nodes : 8
Number of edges : 12
Number of colors : 2
Using the Welsh-Powell algorithm:
Example 1
Edge 0-1 -> Color 0, 1
Edge 1-2 -> Color 1, 2
Edge 2-0 -> Color 2, 0
Node 3 -> Color 0
Number of nodes : 4
Number of edges : 3
Number of colors : 3
Example 2
Edge 1-6 -> Color 0, 1
Edge 1-7 -> Color 0, 1
Edge 1-8 -> Color 0, 1
Edge 2-5 -> Color 0, 1
Edge 2-7 -> Color 0, 1
Edge 2-8 -> Color 0, 1
Edge 3-5 -> Color 0, 1
Edge 3-6 -> Color 0, 1
Edge 3-8 -> Color 0, 1
Edge 4-5 -> Color 0, 1
Edge 4-6 -> Color 0, 1
Edge 4-7 -> Color 0, 1
Number of nodes : 8
Number of edges : 12
Number of colors : 2
Example 3
Edge 1-4 -> Color 0, 1
Edge 1-6 -> Color 0, 2
Edge 1-8 -> Color 0, 3
Edge 3-2 -> Color 1, 0
Edge 3-6 -> Color 1, 2
Edge 3-8 -> Color 1, 3
Edge 5-2 -> Color 2, 0
Edge 5-4 -> Color 2, 1
Edge 5-8 -> Color 2, 3
Edge 7-2 -> Color 3, 0
Edge 7-4 -> Color 3, 1
Edge 7-6 -> Color 3, 2
Number of nodes : 8
Number of edges : 12
Number of colors : 4
Example 4
Edge 1-6 -> Color 0, 1
Edge 7-1 -> Color 1, 0
Edge 8-1 -> Color 1, 0
Edge 5-2 -> Color 1, 0
Edge 2-7 -> Color 0, 1
Edge 2-8 -> Color 0, 1
Edge 3-5 -> Color 0, 1
Edge 6-3 -> Color 1, 0
Edge 3-8 -> Color 0, 1
Edge 4-5 -> Color 0, 1
Edge 4-6 -> Color 0, 1
Edge 4-7 -> Color 0, 1
Number of nodes : 8
Number of edges : 12
Number of colors : 2
Julia
Uses a repeated randomization of node color ordering to seek a minimum number of colors needed. <lang julia>using Random
"""Useful constants for the colors to be selected for nodes of the graph""" const colors4 = ["blue", "red", "green", "yellow"] const badcolor = "black" @assert(!(badcolor in colors4))
"""
struct graph
undirected simple graph constructed from its name and a string listing of point to point connections """ mutable struct Graph
name::String
g::Dict{Int, Vector{Int}}
nodecolor::Dict{Int, String}
function Graph(nam::String, s::String)
gdic = Dict{Int, Vector{Int}}()
for p in eachmatch(r"(\d+)-(\d+)|(\d+)(?!\s*-)" , s)
if p != nothing
if p[3] != nothing
n3 = parse(Int, p[3])
get!(gdic, n3, [])
else
n1, n2 = parse(Int, p[1]), parse(Int, p[2])
p1vec = get!(gdic, n1, [])
!(n2 in p1vec) && push!(p1vec, n2)
p2vec = get!(gdic, n2, [])
!(n1 in p2vec) && push!(p2vec, n1)
end
end
end
new(nam, gdic, Dict{Int, String}())
end
end
"""
tryNcolors!(gr::Graph, N, maxtrials)
Try up to maxtrials to get a coloring with <= N colors """ function tryNcolors!(gr::Graph, N, maxtrials)
t, mintrial, minord = N, N, Dict()
for _ in 1:maxtrials
empty!(gr.nodecolor)
ordering = shuffle(collect(keys(gr.g)))
for node in ordering
usedneighborcolors = [gr.nodecolor[c] for c in gr.g[node] if haskey(gr.nodecolor, c)]
gr.nodecolor[node] = badcolor
for c in colors4[1:N]
if !(c in usedneighborcolors)
gr.nodecolor[node] = c
break
end
end
end
t = length(unique(values(gr.nodecolor)))
if t <= mintrial
mintrial = t
minord = deepcopy(gr.nodecolor)
end
end
if length(minord) > 0
gr.nodecolor = minord
end
end
"""
prettyprintcolors(gr::graph)
print out the colored nodes in graph """ function prettyprintcolors(gr::Graph)
println("\nColors for the graph named ", gr.name, ":")
edgesdone = Vector{Vector{Int}}()
for (node, neighbors) in gr.g
if !isempty(neighbors)
for n in neighbors
edge = node < n ? [node, n] : [n, node]
if !(edge in edgesdone)
println(" ", edge[1], "-", edge[2], " Color: ",
gr.nodecolor[edge[1]], ", ", gr.nodecolor[edge[2]])
push!(edgesdone, edge)
end
end
else
println(" ", node, ": ", gr.nodecolor[node])
end
end
println("\n", length(unique(keys(gr.nodecolor))), " nodes, ",
length(edgesdone), " edges, ",
length(unique(values(gr.nodecolor))), " colors.")
end
for (name, txt) in [("Ex1", "0-1 1-2 2-0 3"),
("Ex2", "1-6 1-7 1-8 2-5 2-7 2-8 3-5 3-6 3-8 4-5 4-6 4-7"),
("Ex3", "1-4 1-6 1-8 3-2 3-6 3-8 5-2 5-4 5-8 7-2 7-4 7-6"),
("Ex4", "1-6 7-1 8-1 5-2 2-7 2-8 3-5 6-3 3-8 4-5 4-6 4-7")]
exgraph = Graph(name, txt)
tryNcolors!(exgraph, 4, 100)
prettyprintcolors(exgraph)
end
</lang>
- Output:
Colors for the graph named Ex1:
0-1 Color: red, blue
0-2 Color: red, green
1-2 Color: blue, green
3: blue
4 nodes, 3 edges, 3 colors.
Colors for the graph named Ex2:
1-7 Color: red, blue
2-7 Color: red, blue
4-7 Color: red, blue
4-5 Color: red, blue
4-6 Color: red, blue
2-5 Color: red, blue
2-8 Color: red, blue
3-5 Color: red, blue
3-6 Color: red, blue
3-8 Color: red, blue
1-8 Color: red, blue
1-6 Color: red, blue
8 nodes, 12 edges, 2 colors.
Colors for the graph named Ex3:
2-7 Color: red, blue
4-7 Color: red, blue
6-7 Color: red, blue
1-4 Color: blue, red
4-5 Color: red, blue
2-3 Color: red, blue
2-5 Color: red, blue
3-6 Color: blue, red
3-8 Color: blue, red
1-8 Color: blue, red
5-8 Color: blue, red
1-6 Color: blue, red
8 nodes, 12 edges, 2 colors.
Colors for the graph named Ex4:
1-7 Color: red, blue
2-7 Color: red, blue
4-7 Color: red, blue
4-5 Color: red, blue
4-6 Color: red, blue
2-5 Color: red, blue
2-8 Color: red, blue
3-5 Color: red, blue
3-6 Color: red, blue
3-8 Color: red, blue
1-8 Color: red, blue
1-6 Color: red, blue
8 nodes, 12 edges, 2 colors.
Perl 6
<lang perl6>#!/usr/bin/env perl6
sub GraphNodeColor(@RAW) {
my %OneMany = my %NodeColor;
for @RAW { %OneMany{$_[0]}.push: $_[1] ; %OneMany{$_[1]}.push: $_[0] }
my @ColorPool = "0", "1" … ^+%OneMany.elems; # as string
my %NodePool = %OneMany.keys.SetHash;
if %OneMany<NaN>:exists { # skip islanders for now
%NodePool{$_}:delete for @(%OneMany<NaN>);
%NodePool<NaN>:delete;
}
while %NodePool.Bool {
my $color = @ColorPool.grab;
my %TempPool = %NodePool;
while (my \n = %TempPool.pick.key) {
%NodeColor{n} = $color;
%TempPool{n}:delete;
%TempPool{$_}:delete for @(%OneMany{n}) ; # skip neighbors as well
%NodePool{n}:delete;
}
}
if %OneMany<NaN>:exists { # islanders use an existing color
%NodeColor{$_} = %NodeColor.pick.value for @(%OneMany<NaN>)
}
return %NodeColor
}
my \DATA = [
[<0 1>,<1 2>,<2 0>,<3 NaN>,<4 NaN>,<5 NaN>], [<1 6>,<1 7>,<1 8>,<2 5>,<2 7>,<2 8>,<3 5>,<3 6>,<3 8>,<4 5>,<4 6>,<4 7>], [<1 4>,<1 6>,<1 8>,<3 2>,<3 6>,<3 8>,<5 2>,<5 4>,<5 8>,<7 2>,<7 4>,<7 6>], [<1 6>,<7 1>,<8 1>,<5 2>,<2 7>,<2 8>,<3 5>,<6 3>,<3 8>,<4 5>,<4 6>,<4 7>],
];
for DATA {
say "DATA : ", $_;
say "Result : ";
my %out = GraphNodeColor $_;
say "$_[0]-$_[1]:\t Color %out{$_[0]} ",$_[1].isNaN??!!%out{$_[1]} for @$_;
say "Nodes : ", %out.keys.elems;
say "Edges : ", $_.elems;
say "Colors : ", %out.values.Set.elems;
}</lang>
- Output:
DATA : [(0 1) (1 2) (2 0) (3 NaN) (4 NaN) (5 NaN)] Result : 0-1: Color 2 4 1-2: Color 4 3 2-0: Color 3 2 3-NaN: Color 4 4-NaN: Color 3 5-NaN: Color 2 Nodes : 6 Edges : 6 Colors : 3 DATA : [(1 6) (1 7) (1 8) (2 5) (2 7) (2 8) (3 5) (3 6) (3 8) (4 5) (4 6) (4 7)] Result : 1-6: Color 5 6 1-7: Color 5 6 1-8: Color 5 6 2-5: Color 5 6 2-7: Color 5 6 2-8: Color 5 6 3-5: Color 5 6 3-6: Color 5 6 3-8: Color 5 6 4-5: Color 5 6 4-6: Color 5 6 4-7: Color 5 6 Nodes : 8 Edges : 12 Colors : 2 DATA : [(1 4) (1 6) (1 8) (3 2) (3 6) (3 8) (5 2) (5 4) (5 8) (7 2) (7 4) (7 6)] Result : 1-4: Color 3 6 1-6: Color 3 6 1-8: Color 3 6 3-2: Color 3 6 3-6: Color 3 6 3-8: Color 3 6 5-2: Color 3 6 5-4: Color 3 6 5-8: Color 3 6 7-2: Color 3 6 7-4: Color 3 6 7-6: Color 3 6 Nodes : 8 Edges : 12 Colors : 2 DATA : [(1 6) (7 1) (8 1) (5 2) (2 7) (2 8) (3 5) (6 3) (3 8) (4 5) (4 6) (4 7)] Result : 1-6: Color 1 5 7-1: Color 5 1 8-1: Color 5 1 5-2: Color 5 1 2-7: Color 1 5 2-8: Color 1 5 3-5: Color 1 5 6-3: Color 5 1 3-8: Color 1 5 4-5: Color 1 5 4-6: Color 1 5 4-7: Color 1 5 Nodes : 8 Edges : 12 Colors : 2
Python
<lang python>import re from collections import defaultdict from itertools import count
connection_re = r"""
(?: (?P<N1>\d+) - (?P<N2>\d+) | (?P<N>\d+) (?!\s*-)) """
class Graph:
def __init__(self, name, connections):
self.name = name
self.connections = connections
g = self.graph = defaultdict(list) # maps vertex to direct connections
matches = re.finditer(connection_re, connections,
re.MULTILINE | re.VERBOSE)
for match in matches:
n1, n2, n = match.groups()
if n:
g[n] += []
else:
g[n1].append(n2) # Each the neighbour of the other
g[n2].append(n1)
def greedy_colour(self, order=None):
"Greedy colourisation algo."
if order is None:
order = self.graph # Choose something
colour = self.colour = {}
neighbours = self.graph
for node in order:
used_neighbour_colours = (colour[nbr] for nbr in neighbours[node]
if nbr in colour)
colour[node] = first_avail_int(used_neighbour_colours)
self.pp_colours()
return colour
def pp_colours(self):
print(f"\n{self.name}")
c = self.colour
e = canonical_edges = set()
for n1, neighbours in sorted(self.graph.items()):
if neighbours:
for n2 in neighbours:
edge = tuple(sorted([n1, n2]))
if edge not in canonical_edges:
print(f" {n1}-{n2}: Colour: {c[n1]}, {c[n2]}")
canonical_edges.add(edge)
else:
print(f" {n1}: Colour: {c[n1]}")
lc = len(set(c.values()))
print(f" #Nodes: {len(c)}\n #Edges: {len(e)}\n #Colours: {lc}")
def first_avail_int(data):
"return lowest int 0... not in data"
d = set(data)
for i in count():
if i not in d:
return i
if __name__ == '__main__':
for name, connections in [
('Ex1', "0-1 1-2 2-0 3"),
('Ex2', "1-6 1-7 1-8 2-5 2-7 2-8 3-5 3-6 3-8 4-5 4-6 4-7"),
('Ex3', "1-4 1-6 1-8 3-2 3-6 3-8 5-2 5-4 5-8 7-2 7-4 7-6"),
('Ex4', "1-6 7-1 8-1 5-2 2-7 2-8 3-5 6-3 3-8 4-5 4-6 4-7"),
]:
g = Graph(name, connections)
g.greedy_colour()</lang>
- Output:
Ex1
0-1: Colour: 0, 1
0-2: Colour: 0, 2
1-2: Colour: 1, 2
3: Colour: 0
#Nodes: 4
#Edges: 3
#Colours: 3
Ex2
1-6: Colour: 0, 1
1-7: Colour: 0, 1
1-8: Colour: 0, 1
2-5: Colour: 0, 1
2-7: Colour: 0, 1
2-8: Colour: 0, 1
3-5: Colour: 0, 1
3-6: Colour: 0, 1
3-8: Colour: 0, 1
4-5: Colour: 0, 1
4-6: Colour: 0, 1
4-7: Colour: 0, 1
#Nodes: 8
#Edges: 12
#Colours: 2
Ex3
1-4: Colour: 0, 1
1-6: Colour: 0, 1
1-8: Colour: 0, 1
2-3: Colour: 1, 0
2-5: Colour: 1, 0
2-7: Colour: 1, 0
3-6: Colour: 0, 1
3-8: Colour: 0, 1
4-5: Colour: 1, 0
4-7: Colour: 1, 0
5-8: Colour: 0, 1
6-7: Colour: 1, 0
#Nodes: 8
#Edges: 12
#Colours: 2
Ex4
1-6: Colour: 0, 1
1-7: Colour: 0, 1
1-8: Colour: 0, 1
2-5: Colour: 2, 0
2-7: Colour: 2, 1
2-8: Colour: 2, 1
3-5: Colour: 2, 0
3-6: Colour: 2, 1
3-8: Colour: 2, 1
4-5: Colour: 2, 0
4-6: Colour: 2, 1
4-7: Colour: 2, 1
#Nodes: 8
#Edges: 12
#Colours: 3
Python dicts preserve insertion order and Ex2/Ex3 edges are traced in a similar way which could be the cause of exactly the same colours used for Ex2 and Ex3. The wp article must use an earlier version of Python/different ordering of edge definitions.
Ex4 changes the order of nodes enough to affect the number of colours used.