Solutions can be found
here.
A preliminary remark: The vocabulary in graph theory varies considerably. Some authors use the same word with different meanings. Some authors use different words to mean the same thing. I hope that our definitions are free of contradictions.
A graph is defined as a set of nodes
and a set of edges, where each edge is a pair of nodes.
There are several ways to represent graphs in Prolog.
One method is to
represent each edge separately as one clause (fact). In this form,
the graph depicted opposite is represented as the following predicate:
edge(h,g). edge(k,f). edge(f,b). ...
We call this edgeclause form.
Obviously,
isolated nodes cannot be represented.
Another method is to represent the whole graph as one data object. According
to the definition of the graph as a pair of two sets (nodes and edges), we
may use the following Prolog term to represent the above example graph:
graph([b,c,d,f,g,h,k],[e(b,c),e(b,f),e(c,f),e(f,k),e(g,h)])
We call this graphterm form. Note, that the lists are kept sorted,
they are really sets, without duplicated elements. Each edge
appears only once in the edge list; i.e. an edge from
a node x to another node y is represented as e(x,y), the term e(y,x)
is not present. The graphterm form is our default representation.
In SWIProlog there are predefined predicates to work
with sets.
A third representation method is to associate with each node the set
of nodes that are adjacent to that node. We call this the
adjacencylist form. In our example:
[n(b,[c,f]), n(c,[b,f]), n(d,[]), n(f,[b,c,k]), ...]
The representations we introduced so far are Prolog terms and therefore
well suited for automated processing, but their syntax is not very
userfriendly. Typing the terms by hand is cumbersome and errorprone.
We can define a more compact and "humanfriendly" notation
as follows: A graph is represented by a list of atoms and terms of
the type XY (i.e. functor '' and arity 2). The atoms stand for
isolated nodes, the XY terms describe edges. If an X appears as an
endpoint of an edge, it is automatically defined as a node.
Our example could be written as:
[bc, fc, gh, d, fb, kf, hg]
We call this the humanfriendly form. As the example shows,
the list does not have to be sorted and may even contain the same
edge multiple times. Notice the isolated node d. (Actually, isolated
nodes do not even have to be atoms in the Prolog sense, they can
be compound terms, as in d(3.75,blue) instead of d in the example).
When the edges are directed we call them arcs. These
are represented by ordered pairs. Such a graph is called
directed graph (or digraph, for short). To represent a directed graph, the forms
discussed above are slightly modified. The example graph opposite
is represented as follows:
 Arcclause form
 arc(s,u).
arc(u,r).
...
 Graphterm form
 digraph([r,s,t,u,v],[a(s,r),a(s,u),a(u,r),a(u,s),a(v,u)])
 Adjacencylist form
 [n(r,[]),n(s,[r,u]),n(t,[]),n(u,[r]),n(v,[u])]
Note that the adjacencylist does not have the information on whether
it is a graph or a digraph.
 Humanfriendly form
 [s > r, t, u > r, s > u, u > s, v > u]
Finally, graphs and digraphs may have additional information attached
to nodes and edges (arcs). For the nodes, this is no problem, as we can
easily replace the single character identifiers with arbitrary compound
terms, such as city('London',4711). On the other hand, for
edges we have to extend our notation. Graphs with additional information
attached to edges are called labeled graphs.
 Arcclause form
 arc(m,q,7).
arc(p,q,9).
arc(p,m,5).
 Graphterm form
 digraph([k,m,p,q],[a(m,p,7),a(p,m,5),a(p,q,9)])
 Adjacencylist form
 [n(k,[]),n(m,[q/7]),n(p,[m/5,q/9]),n(q,[])]
Notice how the edge information has been packed into a term with
functor '/' and arity 2, together with the corresponding node.
 Humanfriendly form
 [p>q/9, m>q/7, k, p>m/5]
The notation for labeled graphs can also be used for socalled
multigraphs, where more than one edge (or arc) are allowed
between two given nodes.
 6.01
(***) Conversions
 Write predicates to convert between the different graph
representations. With these predicates, all representations
are equivalent; i.e. for the following problems you can always freely pick the most convenient form. The reason this problem is rated (***) is
not because it's particularly difficult, but because it's a lot
of work to deal with all the special cases.
 6.02
(**) Path from one node to another one
 Write a predicate path(G,A,B,P) to find an acyclic path P from
node A to node B in the graph G. The predicate should return all paths
via backtracking.
 6.03
(*) Cycle from a given node
 Write a predicate cycle(G,A,P) to find a closed path (cycle) P
starting at a given node A in the graph G. The predicate should
return all cycles via backtracking.
 6.04
(**) Construct all spanning trees

 Write a predicate s_tree(Graph,Tree) to construct
(by backtracking) all spanning trees
of a given graph. With this predicate, find out how many
spanning trees there are for the graph depicted to the left.
The data of this example graph can be found in the file p6_04.dat.
When you have a correct solution for the s_tree/2 predicate, use it to
define two other useful predicates: is_tree(Graph) and
is_connected(Graph). Both are fiveminutes tasks!
 6.05
(**) Construct the minimal spanning tree
 Write a predicate ms_tree(Graph,Tree,Sum) to construct
the minimal spanning tree of a given labelled graph. Hint:
Use the algorithm of Prim. A small modification of the solution of 6.04 does the trick. The data of the example graph to the right can
be found in the file p6_05.dat.
 6.06
(**) Graph isomorphism
 Two graphs G1(N1,E1) and G2(N2,E2) are isomorphic if there is
a bijection f: N1 > N2 such that for any nodes X,Y of N1, X and Y
are adjacent if and only if f(X) and f(Y) are adjacent.
Write a predicate that determines whether two graphs are isomorphic.
Hint: Use an openended list to represent the function f.
 6.07
(**) Node degree and graph coloration
 a) Write a predicate degree(Graph,Node,Deg) that determines
the degree of a given node.
b) Write a predicate that generates a list of all nodes of a
graph sorted according to decreasing degree.
c) Use WelchPowell's algorithm to paint the nodes of a graph
in such a way that adjacent nodes have different colors.
 6.08
(**) Depthfirst order graph traversal
 Write a predicate that generates a depthfirst order graph
traversal sequence. The starting point should be specified,
and the output should be a list of nodes that are reachable from
this starting point (in depthfirst order).
 6.09
(**) Connected components
 Write a predicate that splits a graph into its connected components.
 6.10
(**) Bipartite graphs
 Write a predicate that finds out whether a given graph is bipartite.

 6.11
(***) Generate Kregular simple graphs with N nodes
 In a Kregular graph all nodes have a degree of K; i.e. the number
of edges incident in each node is K. How many (nonisomorphic!) 3regular
graphs with 6 nodes are there?
 See also the table of results
in p6_11.txt.
