5.2.1. How to start

Learning objectives

The class graph
Creating nodes and edges
The classes node_array and edge_array
Using basic graph algorithms
Topological sorting with TOPSORT()
Iterating over all nodes and edges

Every author who starts to describe a connected topic complex, such as the LEDA system itself, must face the following, extremely important question, which is often hard to answer: "Oh my God, where shall I begin?" The problem is that the individual topics are dependent on each other: To understand what is said in section Y, the reader must have read section X first. Every good author takes care to arrange the sections so that the reader can read through the complete works from the front to behind once and must never already know on page x what stands written only later on page y. So the author should get by without forward references; then the reader gets by on one single pass through the text. Many otherwise outstanding works lack exactly this quality, what makes them difficult to understand for the non-adept.

How can the sections be arranged in that way? First of all, it makes sense to model the problem definition as a graph: For every section a node is introduced. If the contents of section X are a precondition for the understanding of the contents of section Y, then an edge (X, Y) from X to Y is drawn. The graph arising this way is called a dependency graph. Figure 5.4 shows such a dependency graph for a topic complex consisting of 8 sections.

Figure 5.4. A dependency graph

A dependency graph

A dependency graph for 8 sections A, B, C, D, E, F, G, and H, which are to be placed into a literary work. The sections are to be put in order so that a reader can understand the work without ever having to skim forward.

Of course, we should begin our work with a section to which no edges are incident, that is, with a node v whose in-degree is 0. Only such a node does not have other nodes as prerequisite for the understanding. But what if there is no such node? Then we have lost; it is impossible to guide the reader through the work in one single pass: In every section he has already to know the contents of another section. If, in contrast, such a node v exists, we can start with v. We give it the number 1.

But how do we arrange the remaining nodes? Since we have already recognized the section v as the first of a good order, we can remove v from the set of the sections still to be arranged, that is, the node v can be removed from the graph. By this, the in-degree of all nodes previously adjacent to v is decreased by 1. Now the same question as before arises: With which node do we carry on? Again, we search for a node w with in-degree 0. If there is none, we have lost. Otherwise, we give it the number 2, remove it from the graph, and continue in this way until we either are left with an empty graph (and have given all nodes a number) or have stopped at a non-empty graph in which at least one edge leads to every node.

In case we have been able to assign a number N(v) to every node v, we have won: We arrange the sections of our work in the order given by the N(v)'s. This numbering N has a special characteristic: Whenever there is an edge (u,v) in the original graph, the number N(u) is smaller than the number N(v). A numbering with such a characteristic is called a topological sorting.

Exactly when can such a topological sorting be found? As one may easily suspect, the existence of such a sorting has to do with the existence of cycles in a graph. Exercise 48 asks to work out that a topological sorting exists if and only if a graph does not have any cycles.

A first program

Sorting a graph topologically is a basic graph algorithm problem. Of course, LEDA has a corresponding built-in function. As a first program, we want to build up the graph from Figure 5.4 by means of the structure that LEDA provides for this, and calculate a topological sorting.

LEDA's class for managing graphs is called, how wonder, graph. It is it an extremely powerful class, whose methods we will get to know one after the other.

A graph is created by

graph G;

First of all, to build up the structure of the graph, nodes must be inserted. This is done by a call of

node v = G.new_node();

This method returns an item of type node, which is a dependent item type. The corresponding node can be accessed later only via such an item; for this purpose, the returned item can be saved in a variable of type node. The node can be deleted from the graph at a later time via this item by a call of


This removes also all leaving and incident edges of this node.

An edge can be inserted between two nodes v1 and v2 by

edge e = G.new_edge(v1, v2);

Also here, a dependent item is returned; it is of type edge. Later accesses to the edge just inserted are possible only via such an item. For example, the edge can be deleted from the graph by


With the methods new_node() and new_edge(), the combinatorial structure of a given graph can be constructed successively. The graph, however, still has no information associated! But it could be desirable to give every node a name, for example, the name of a corresponding section in a Pulitzer-Price suspicious tutorial on LEDA. And after all, the number of a topological sorting shall be stored with every node, too! Here a way to associate some information to a node is necessary. For this purpose, LEDA has ready the class node_array (among other classes for the same purpose). This class is used, like an ordinary array, via the subscript operator []. Here the key indices are always of type node. Correspondingly, there is the class edge_array, with which information can be associated to the edges of a graph.


There are only very few problem definitions that can be solved with the class graph alone. As we have said, this class merely serves for the coding of the combinatorial structure of a graph. In most cases, additional classes, such as node_array or edge_array, are needed to hold additional information.

On a graph already constructed, a node_array for the numbering of a topological sorting is defined as follows:

node_array<int> top_num(G);

(Node arrays will be explained in detail in Section 5.2.2.)

The LEDA function TOPSORT() for calculating a topological sorting is used as follows:

bool has_top_sort = TOPSORT(G, top_num);

It returns false if the graph is cyclic and therefore does not have a topological sorting. (We will get to know a possible implementation of this function in Section 5.2.7.) The following program first builds up the above graph and then calls TOPSORT() to calculate a topological sorting.


If you work under Unix and would like to try out the programs of this chapter, you must keep in mind the following: Under Unix, the class graph and all other classes and algorithms introduced in this chapter (apart from the class GraphWin discussed in Section 5.2.5) are contained in the LEDA library libG. For this library to be linked in correctly, the programmer must take into account what was said in Section 1.3.1 about linking under Unix: The order -lG -lL -lm must be adhered to.

For GraphWin the libraries libW and libP additionally have to be linked in. The link order must be -lW -lP -lG -lL -lm.

Here is the program:

Filename: AuthorsProblem.C
LEDA users as of version 5.0: Note that header files are now partitioned into modules (subfolders)!
#include <LEDA/graph.h>
#include <LEDA/node_array.h>
#include <LEDA/basic_graph_alg.h>
#include <LEDA/string.h>

using leda::graph;
using leda::node;
using leda::node_array;
using leda::TOPSORT;
using leda::string;
using std::cout;

int main()
  graph G;

  node v0 = G.new_node();
  node v1 = G.new_node();
  node v2 = G.new_node();
  node v3 = G.new_node();
  node v4 = G.new_node();
  node v5 = G.new_node();
  node v6 = G.new_node();
  node v7 = G.new_node();

  G.new_edge(v0, v1);
  G.new_edge(v0, v3);
  G.new_edge(v1, v2);
  G.new_edge(v2, v3);
  G.new_edge(v4, v5);
  G.new_edge(v5, v0);
  G.new_edge(v5, v2);
  G.new_edge(v6, v1);
  G.new_edge(v6, v4);
  G.new_edge(v7, v2);
  G.new_edge(v7, v3);
  G.new_edge(v7, v6);
  G.new_edge(v0, v1);

  node_array<string> name(G);
  name[v0] = "A";
  name[v1] = "B";
  name[v2] = "C";
  name[v3] = "D";
  name[v4] = "E";
  name[v5] = "F";
  name[v6] = "G";
  name[v7] = "H";
  cout << "This graph has " << G.number_of_nodes() << " nodes and ";
  cout                      << G.number_of_edges() << " edges.\n";

  node_array<int> top_num(G);
  if(!TOPSORT(G, top_num)) {
    cout << "G has no topological sorting!\n";
  else {
    node v;

    cout << "The following is a topological sorting:\n";
      cout << name[v] << " " << top_num[v] << "\n";

    cout << "Nodes sorted topologically:\n";
      cout << name[v] << " " << top_num[v] << "\n";

The output of the program reads:

This graph has 8 nodes and 13 edges.
The following is a topological sorting:
A 5
B 6
C 7
D 8
E 3
F 4
G 2
H 1
Nodes sorted topologically:
H 1
G 2
E 3
F 4
A 5
B 6
C 7
D 8

So as a clever author, one could put the sections in the order H, G, E, F, A, B, C, D. This would burden no material not yet covered on the reader, nowhere. So he would never have to skim forwards then! With this method, a reader-friendly order of the sections can always be found, provided that the dependency graph does not have any cycles.[40]

To be able to use the function TOPSORT() or other basic graph algorithms, the header file basic_graph_alg.h must be included.

Since we do not use the edge items returned by new_edge() any further in the above program, we do not store them temporarily.

A call of


returns the current number of nodes and edges of graph.

All nodes of a graph can be traversed by means of the macro

forall_nodes(v, G) {...}

It successively returns items v that refer to the individual nodes of a graph.

Accordingly, the macro

forall_edges(e, G) {...}

iterates over all edges. We will learn the details on how to iterate over graphs in Section 5.2.4.

In the internal representation of a graph, the nodes and edges lie in a certain order; this order is traversed by forall_nodes and forall_edges. The method


reorders the nodes internally by the order that has been passed in the node array numbering. In the previous program, we made use of this method to output the nodes in the order of their topological sorting. Corresponding to sort_nodes(), there is the method sort_edges() for sorting edges.

Implementation and further information

Graphs are implemented by two doubly linked, circular lists, one for the nodes and one for the edges; we will go into the details later. Most operations on a graph take constant time; if not, the required time mostly is obvious from the semantics of the operation.

Of course, the definite reference for the class graph is the corresponding manual page. This page contains a whole wealth of information - the interface of graph is exceptionally great. We will get to know the most important concepts and methods in the next sections step by step.


Exercise 48.

Work out why the following holds true: A directed graph has a topological sorting if and only if it does not contain a cycle.

Work out in addition why the method introduced in this section discovers such a sorting if there is one. Why does a topological sorting not have to be unique?

Exercise 49.

A graph is called planar if it can be drawn into the plane such that its edges do not intersect in any point.

In an old children's game, 3 houses H1, H2 and H3 shall be connected to a waterworks W, an electric power plant E, and a thermal power station H by lines such that the lines do not cross, see Figure 5.5. This graph is called K3,3. We will get to know its significance for drawing graphs in more detail in Section 5.2.6.

Figure 5.5. The graph K3,3

The graph K3,3

The graph K3,3 is not planar: However we draw the (dotted) edge from H3 to W, we must cross one of the other edges. This also applies to every other way to draw the other edges.

Build up this graph with the class graph and use the built-in function Is_Planar() to test whether this graph is planar. To use this function, the header file graph_misc.h has to be included.

[40] What is natural for a computer scientist and needs hardly any explanation to him, namely to arrange the individual sections in the order of a topological sorting when writing scientific texts, seems not be well known in other disciplines. So the author got the following feedback on the application of topological sorting introduced here from a physicist well-known to him who has been working in research for over 10 years and already has published numerous scientific papers: “Well, I'm flabbergasted. Since more then 10 years I've been wondering under great efforts again and again how I should arrange my sections. It was completely unclear to me that this can be done so easily with the help of a graph. You should publish this. This could be pioneering for the publication practice in all disciplines, included the arts.