Loading

In
mathematics and
computer science, **graph theory** is the study of *
graphs*: mathematical structures used to model pairwise
relations between objects from a certain collection. A "graph"
in this context refers to a collection of
vertices or 'nodes' and a collection of *edges* that
connect pairs of vertices. A graph may be *undirected*,
meaning that there is no distinction between the two vertices
associated with each edge, or its edges may be *directed*
from one vertex to another; see
graph (mathematics) for more detailed definitions and for
other variations in the types of graphs that are commonly
considered. The graphs studied in graph theory should not be
confused with "graphs
of functions" and
other
kinds of graphs.

Refer to Glossary of graph theory for basic definitions in graph theory.

The paper written by
Leonhard Euler on the *
Seven Bridges of Königsberg* and published in 1736 is
regarded as the first paper in the history of graph theory.^{[1]}
This paper, as well as the one written by
Vandermonde on the *
knight problem,* carried on with the *analysis situs*
initiated by
Leibniz. Euler's formula relating the number of edges,
vertices, and faces of a convex polyhedron was studied and
generalized by
Cauchy^{[2]}
and
L'Huillier,^{[3]}
and is at the origin of
topology.

More than one century after Euler's paper on the bridges of
Königsberg and while
Listing introduced topology,
Cayley was led by the study of particular analytical forms
arising from
differential calculus to study a particular class of graphs,
the *
trees*. This study had many implications in theoretical
chemistry. The involved techniques mainly concerned the
enumeration of graphs having particular properties.
Enumerative graph theory then rose from the results of Cayley
and the fundamental results published by
Pólya between 1935 and 1937 and the generalization of these
by
De Bruijn in 1959. Cayley linked his results on trees with
the contemporary studies of chemical composition.^{[4]}
The fusion of the ideas coming from mathematics with those
coming from chemistry is at the origin of a part of the standard
terminology of graph theory.

In particular, the term "graph" was introduced by
Sylvester in a paper published in 1878 in *
Nature*, where he draws an analogy between "quantic
invariants" and "co-variants" of algebra and molecular diagrams:^{[5]}

- "[...] Every invariant and co-variant thus becomes
expressible by a
*graph*precisely identical with a Kekuléan diagram or chemicograph. [...] I give a rule for the geometrical multiplication of graphs,*i.e.*for constructing a*graph*to the product of in- or co-variats whose separate graphs are given. [...]" (italics as in the original).

One of the most famous and productive problems of graph
theory is the
four color problem: "Is it true that any map drawn in the
plane may have its regions colored with four colors, in such a
way that any two regions having a common border have different
colors?" This problem was first posed by
Francis Guthrie in 1852 and its first written record is in a
letter of
De Morgan addressed to
Hamilton the same year. Many incorrect proofs have been
proposed, including those by Cayley,
Kempe, and others. The study and the generalization of this
problem by
Tait,
Heawood,
Ramsey and
Hadwiger led to the study of the colorings of the graphs
embedded on surfaces with arbitrary
genus. Tait's reformulation generated a new class of
problems, the *factorization problems*, particularly
studied by
Petersen and
Kőnig. The works of Ramsey on colorations and more specially
the results obtained by
Turán in 1941 was at the origin of another branch of graph
theory, *
extremal graph theory*.

The four color problem remained unsolved for more than a
century. A proof produced in 1976 by
Kenneth Appel and
Wolfgang Haken,^{[6]}^{[7]}
which involved checking the properties of 1,936 configurations
by computer, was not fully accepted at the time due to its
complexity. A simpler proof considering only 633 configurations
was given twenty years later by
Robertson,
Seymour,
Sanders and
Thomas.^{[8]}

The autonomous development of topology from 1860 and 1930 fertilized graph theory back through the works of Jordan, Kuratowski and Whitney. Another important factor of common development of graph theory and topology came from the use of the techniques of modern algebra. The first example of such a use comes from the work of the physicist Gustav Kirchhoff, who published in 1845 his Kirchhoff's circuit laws for calculating the voltage and current in electric circuits.

The introduction of probabilistic methods in graph theory,
especially in the study of
Erdős and
Rényi of the asymptotic probability of graph connectivity,
gave rise to yet another branch, known as *
random graph theory*, which has been a fruitful source of
graph-theoretic results.

Main article:
Graph drawing

Graphs are represented graphically by drawing a dot for every vertex, and drawing an arc between two vertices if they are connected by an edge. If the graph is directed, the direction is indicated by drawing an arrow.

A graph drawing should not be confused with the graph itself (the abstract, non-visual structure) as there are several ways to structure the graph drawing. All that matters is which vertices are connected to which others by how many edges and not the exact layout. In practice it is often difficult to decide if two drawings represent the same graph. Depending on the problem domain some layouts may be better suited and easier to understand than others.

Main article:
Graph (data structure)

There are different ways to store graphs in a computer system. The data structure used depends on both the graph structure and the algorithm used for manipulating the graph. Theoretically one can distinguish between list and matrix structures but in concrete applications the best structure is often a combination of both. List structures are often preferred for sparse graphs as they have smaller memory requirements. Matrix structures on the other hand provide faster access for some applications but can consume huge amounts of memory .

- Incidence list
- The edges are represented by an
array containing pairs (ordered if directed) of vertices
(that the edge connects) and possibly weight and other data.
Vertices connected by an edge are said to be
*adjacent*. - Adjacency list
- Much like the incidence list, each vertex has a list of which vertices it is adjacent to. This causes redundancy in an undirected graph: for example, if vertices A and B are adjacent, A's adjacency list contains B, while B's list contains A. Adjacency queries are faster, at the cost of extra storage space.

- Incidence matrix
- The graph is represented by a
matrix of size |
*V*| (number of vertices) by |*E*| (number of edges) where the entry [vertex, edge] contains the edge's endpoint data (simplest case: 1 - connected, 0 - not connected). - Adjacency matrix
- This is the
*n*by*n*matrix*A*, where*n*is the number of vertices in the graph. If there is an edge from some vertex*x*to some vertex*y*, then the element*a*_{x,y}is 1 (or in general the number of*xy*edges), otherwise it is 0. In computing, this matrix makes it easy to find subgraphs, and to reverse a directed graph. - Laplacian matrix or Kirchhoff matrix or Admittance matrix
- This is defined as
*D*−*A*, where*D*is the diagonal degree matrix. It explicitly contains both adjacency information and degree information. - Distance matrix
- A symmetric
*n*by*n*matrix*D*whose element*d*_{x,y}is the length of a shortest path between*x*and*y*; if there is no such path*d*_{x,y}= infinity. It can be derived from powers of*A*

There is a large literature on graphical enumeration: the problem of counting graphs meeting specified conditions. Some of this work is found in Harary and Palmer (1973).

A common problem, called the
subgraph isomorphism problem, is finding a fixed graph as a
subgraph in a given graph. One reason to be interested in
such a question is that many
graph properties are *hereditary* for subgraphs, which
means that a graph has the property if and only if all subgraphs
have it too. Unfortunately, finding maximal subgraphs of a
certain kind is often an
NP-complete problem.

- Finding the largest complete graph is called the clique problem (NP-complete).

A similar problem is finding induced subgraphs in a given graph. Again, some important graph properties are hereditary with respect to induced subgraphs, which means that a graph has a property if and only if all induced subgraphs also have it. Finding maximal induced subgraphs of a certain kind is also often NP-complete. For example,

- Finding the largest edgeless induced subgraph, or independent set, called the independent set problem (NP-complete).

Still another such problem, the *minor containment problem*,
is to find a fixed graph as a minor of a given graph. A
minor or **subcontraction** of a graph is any graph
obtained by taking a subgraph and contracting some (or no)
edges. Many
graph properties are hereditary for minors, which means that
a graph has a property if and only if all minors have it too. A
famous example:

- A graph is
planar if it contains as a minor neither the
complete bipartite graph
*K*_{3,3}(See the Three-cottage problem) nor the complete graph*K*_{5}.

Another class of problems has to do with the extent to which
various species and generalizations of graphs are determined by
their *point-deleted subgraphs*, for example:

Many problems have to do with various ways of coloring graphs, for example:

- The four-color theorem
- The strong perfect graph theorem
- The Erdős–Faber–Lovász conjecture (unsolved)
- The total coloring conjecture (unsolved)
- The list coloring conjecture (unsolved)
- The Hadwiger conjecture (graph theory) (unsolved)

- Hamiltonian path and cycle problems
- Minimum spanning tree
- Route inspection problem (also called the "Chinese Postman Problem")
- Seven Bridges of Königsberg
- Shortest path problem
- Steiner tree
- Three-cottage problem
- Traveling salesman problem (NP-complete)

There are numerous problems arising especially from applications that have to do with various notions of flows in networks, for example:

Covering problems are specific instances of subgraph-finding problems, and they tend to be closely related to the clique problem or the independent set problem.

Applications of graph theory are primarily, but not exclusively, concerned with labeled graphs and various specializations of these.

Structures that can be represented as graphs are ubiquitous,
and many problems of practical interest can be represented by
graphs. The link structure of a
website could be represented by a directed graph: the
vertices are the web pages available at the website and a
directed edge from page *A* to page *B* exists if and
only if *A* contains a link to *B*. A similar approach
can be taken to problems in travel, biology, computer chip
design, and many other fields. The development of
algorithms to handle graphs is therefore of major interest
in
computer science. There, the
transformation of graphs is often formalized and represented
by
graph rewrite systems. They are either directly used or
properties of the rewrite systems(e.g. confluence) are studied.

A graph structure can be extended by assigning a weight to each edge of the graph. Graphs with weights, or weighted graphs, are used to represent structures in which pairwise connections have some numerical values. For example if a graph represents a road network, the weights could represent the length of each road. A digraph with weighted edges in the context of graph theory is called a network.

Networks have many uses in the practical side of graph theory, network analysis (for example, to model and analyze traffic networks). Within network analysis, the definition of the term "network" varies, and may often refer to a simple graph.

Many applications of graph theory exist in the form of network analysis. These split broadly into three categories:

- First, analysis to determine structural properties of a network, such as the distribution of vertex degrees and the diameter of the graph. A vast number of graph measures exist, and the production of useful ones for various domains remains an active area of research.
- Second, analysis to find a measurable quantity within the network, for example, for a transportation network, the level of vehicular flow within any portion of it.
- Third, analysis of dynamical properties of networks.

Graph theory is also used to study molecules in chemistry and physics. In condensed matter physics, the three dimensional structure of complicated simulated atomic structures can be studied quantitatively by gathering statistics on graph-theoretic properties related to the topology of the atoms. For example, Franzblau's shortest-path (SP) rings. In chemistry a graph makes a natural model for a molecule, where vertices represent atoms and edges bonds. This approach is especially used in computer processing of molecular structures, ranging from chemical editors to database searching.

Graph theory is also widely used in sociology as a way, for example, to measure actors' prestige or to explore diffusion mechanisms, notably through the use of social network analysis software.

Likewise, graph theory is useful in biology and conservation efforts where a vertex can represent regions where certain species exist (or habitats) and the edges represent migration paths, or movement between the regions. This information is important when looking at breeding patterns or tracking the spread of disease, parasites or how changes to the movement can affect other species.

- Gallery of named graphs
- Glossary of graph theory
- List of graph theory topics
- Publications in graph theory

- Graph property
- Algebraic graph theory
- Conceptual graph
- Data structure
- Disjoint-set data structure
- Entitative graph
- Existential graph
- Graph data structure
- Graph algebras
- Graph automorphism
- Graph coloring
- Graph database
- Graph drawing
- Graph equation
- Graph rewriting
- Logical graph
- Loop
- Null graph
- Quantum graph
- Spectral graph theory
- Strongly regular graphs
- Symmetric graphs
- Tree data structure

- Bellman-Ford algorithm
- Dijkstra's algorithm
- Ford-Fulkerson algorithm
- Kruskal's algorithm
- Nearest neighbour algorithm
- Prim's algorithm
- Depth-first search
- Breadth-first search

- Algebraic graph theory
- Geometric graph theory
- Extremal graph theory
- Probabilistic graph theory
- Topological graph theory

- Berge, Claude
- Bollobás, Béla
- Chung, Fan
- Dirac, Gabriel Andrew
- Erdős, Paul
- Euler, Leonhard
- Faudree, Ralph
- Graham, Ronald
- Harary, Frank
- Heawood, Percy John
- Kőnig, Dénes
- Lovász, László
- Nešetřil, Jaroslav
- Rényi, Alfréd
- Ringel, Gerhard
- Robertson, Neil
- Seymour, Paul
- Szemerédi, Endre
- Thomas, Robin
- Thomassen, Carsten
- Turán, Pál
- Tutte, W. T.

**^**Biggs, N.; Lloyd, E. and Wilson, R. (1986).*Graph Theory, 1736-1936*. Oxford University Press.**^**Cauchy, A.L. (1813). "Recherche sur les polyèdres - premier mémoire".*Journal de l'Ecole Polytechnique***9 (Cahier 16)**: 66–86.**^**L'Huillier, S.-A.-J. (1861). "Mémoire sur la polyèdrométrie".*Annales de Mathématiques***3**: 169–189.**^**Cayley, A. (1875). "Ueber die Analytischen Figuren, welche in der Mathematik Bäume genannt werden und ihre Anwendung auf die Theorie chemischer Verbindungen".*Berichte der deutschen Chemischen Gesellschaft***8**: 1056–1059. doi: .**^**John Joseph Sylvester (1878),*Chemistry and Algebra*. Nature, volume 17, page 284. doi:10.1038/017284a0. Online version accessed on 2009-12-30.**^**Appel, K. and Haken, W. (1977). "Every planar map is four colorable. Part I. Discharging".*Illinois J. Math.***21**: 429–490.**^**Appel, K. and Haken, W. (1977). "Every planar map is four colorable. Part II. Reducibility".*Illinois J. Math.***21**: 491–567.**^**Robertson, N.; Sanders, D.; Seymour, P. and Thomas, R. (1997). "The four color theorem".*Journal of Combinatorial Theory Series B***70**: 2–44. doi: .

- Gibbons,
Alan (1985),
*Algorithmic Graph Theory*, Cambridge University Press . -
Berge, Claude (1958),
*Théorie des graphes et ses applications*, Collection Universitaire de Mathématiques,**II**, Paris: Dunod . English edition, Wiley 1961; Methuen & Co, New York 1962; Russian, Moscow 1961; Spanish, Mexico 1962; Roumanian, Bucharest 1969; Chinese, Shanghai 1963; Second printing of the 1962 first English edition, Dover, New York 2001. -
Chartrand, Gary (1985),
*Introductory Graph Theory*, Dover, ISBN 0-486-24775-9 . -
Biggs, N.; Lloyd, E.; Wilson, R. (1986),
*Graph Theory, 1736–1936*, Oxford University Press . -
Harary, Frank (1969),
*Graph Theory*, Reading, MA: Addison-Wesley . -
Harary, Frank; Palmer, Edgar M. (1973),
*Graphical Enumeration*, New York, NY: Academic Press .

- Graph Theory with Applications (1976) by Bondy and Murty
- Encyclopaedia Britannica, Graph Theory
- Phase Transitions in Combinatorial Optimization Problems, Section 3: Introduction to Graphs (2006) by Hartmann and Weigt
- Digraphs: Theory Algorithms and Applications 2007 by Jorgen Bang-Jensen and Gregory Gutin
- Graph Theory, by Reinhard Diestel

- Graph theory tutorial
- Image gallery: graphs
- Concise, annotated list of graph theory resources for researchers

The content of this section is licensed under the GNU Free Documentation License (local copy). It uses material from the Wikipedia article "Graph theory" modified November 23, 2009 with previous authors listed in its history.