When modelling relations between two different classes of objects, bipartite graphs very often arise naturally.
For instance, a graph of football players and clubs, with an edge between a player and a club if the player has played for that club, is a natural example of an affiliation network, a type of bipartite graph used in social network analysis.
Another example where bipartite graphs appear naturally is in the (NP-complete) railway optimization problem, in which the input is a schedule of trains and their stops, and the goal is to find a set of train stations as small as possible such that every train visits at least one of the chosen stations.
This problem can be modeled as a dominating set problem in a bipartite graph that has a vertex for each train and each station and an edge for each pair of a station and a train that stops at that station.
A third example is in the academic field of numismatics.
Ancient coins are made using two positive impressions of the design (the obverse and reverse).
The charts numismatists produce to represent the production of coins are bipartite graphs.
More abstract examples include the following:
Bipartite graphs may be characterized in several different ways:
- A graph is bipartite if and only if it does not contain an odd cycle.
- A graph is bipartite if and only if it is 2-colorable, (i.e. its chromatic number is less than or equal to 2).
- The spectrum of a graph is symmetric if and only if it's a bipartite graph.
Kőnig's theorem and perfect graphs
An alternative and equivalent form of this theorem is that the size of the maximum independent set plus the size of the maximum matching is equal to the number of vertices.
Combining this equality with Kőnig's theorem leads to the facts that, in bipartite graphs, the size of the minimum edge cover is equal to the size of the maximum independent set, and the size of the minimum edge cover plus the size of the minimum vertex cover is equal to the number of vertices.
Another class of related results concerns perfect graphs: every bipartite graph, the complement of every bipartite graph, the line graph of every bipartite graph, and the complement of the line graph of every bipartite graph, are all perfect.
Perfection of bipartite graphs is easy to see (their chromatic number is two and their maximum clique size is also two) but perfection of the complements of bipartite graphs is less trivial, and is another restatement of Kőnig's theorem.
This was one of the results that motivated the initial definition of perfect graphs.
Perfection of the complements of line graphs of perfect graphs is yet another restatement of Kőnig's theorem, and perfection of the line graphs themselves is a restatement of an earlier theorem of Kőnig, that every bipartite graph has an edge coloring using a number of colors equal to its maximum degree.
According to the strong perfect graph theorem, the perfect graphs have a forbidden graph characterization resembling that of bipartite graphs: a graph is bipartite if and only if it has no odd cycle as a subgraph, and a graph is perfect if and only if it has no odd cycle or its complement as an induced subgraph.
The bipartite graphs, line graphs of bipartite graphs, and their complements form four out of the five basic classes of perfect graphs used in the proof of the strong perfect graph theorem.
The bipartite realization problem is the problem of finding a simple bipartite graph with the degree sequence being two given lists of natural numbers.
(Trailing zeros may be ignored since they are trivially realized by adding an appropriate number of isolated vertices to the digraph.)
Relation to hypergraphs and directed graphs
The main idea is to assign to each vertex the color that differs from the color of its parent in the depth-first search forest, assigning colors in a preorder traversal of the depth-first-search forest.
This will necessarily provide a two-coloring of the spanning forest consisting of the edges connecting vertices to their parents, but it may not properly color some of the non-forest edges.
In a depth-first search forest, one of the two endpoints of every non-forest edge is an ancestor of the other endpoint, and when the depth first search discovers an edge of this type it should check that these two vertices have different colors.
If they do not, then the path in the forest from ancestor to descendant, together with the miscolored edge, form an odd cycle, which is returned from the algorithm together with the result that the graph is not bipartite.
However, if the algorithm terminates without detecting an odd cycle of this type, then every edge must be properly colored, and the algorithm returns the coloring together with the result that the graph is bipartite.
Alternatively, a similar procedure may be used with breadth-first search in place of depth-first search.
Again, each node is given the opposite color to its parent in the search forest, in breadth-first order.
If, when a vertex is colored, there exists an edge connecting it to a previously-colored vertex with the same color, then this edge together with the paths in the breadth-first search forest connecting its two endpoints to their lowest common ancestor forms an odd cycle.
If the algorithm terminates without finding an odd cycle in this way, then it must have found a proper coloring, and can safely conclude that the graph is bipartite.
Odd cycle transversal
Main article: Odd cycle transversal
Odd cycle transversal is an NP-complete algorithmic problem that asks, given a graph G = (V,E) and a number k, whether there exists a set of k vertices whose removal from G would cause the resulting graph to be bipartite.
The problem is fixed-parameter tractable, meaning that there is an algorithm whose running time can be bounded by a polynomial function of the size of the graph multiplied by a larger function of k. The name odd cycle transversal comes from the fact that a graph is bipartite if and only if it has no odd cycles.
Hence, to delete vertices from a graph in order to obtain a bipartite graph, one needs to "hit all odd cycle", or find a so-called odd cycle transversal set.
In the illustration, every odd cycle in the graph contains the blue (the bottommost) vertices, so removing those vertices kills all odd cycles and leaves a bipartite graph.
Main article: Matching (graph theory)
A matching in a graph is a subset of its edges, no two of which share an endpoint.
Polynomial time algorithms are known for many algorithmic problems on matchings, including maximum matching (finding a matching that uses as many edges as possible), maximum weight matching, and stable marriage.
In many cases, matching problems are simpler to solve on bipartite graphs than on non-bipartite graphs, and many matching algorithms such as the Hopcroft–Karp algorithm for maximum cardinality matching work correctly only on bipartite inputs.
The Dulmage–Mendelsohn decomposition is a structural decomposition of bipartite graphs that is useful in finding maximum matchings.
A Tanner graph is a bipartite graph in which the vertices on one side of the bipartition represent digits of a codeword, and the vertices on the other side represent combinations of digits that are expected to sum to zero in a codeword without errors.
In computer science, a Petri net is a mathematical modeling tool used in analysis and simulations of concurrent systems.
A system is modeled as a bipartite directed graph with two sets of nodes: A set of "place" nodes that contain resources, and a set of "event" nodes which generate and/or consume resources.
There are additional constraints on the nodes and edges that constrain the behavior of the system.
Petri nets utilize the properties of bipartite directed graphs and other properties to allow mathematical proofs of the behavior of systems while also allowing easy implementation of simulations of the system.
Corresponding to the geometric property of points and lines that every two lines meet in at most one point and every two points be connected with a single line, Levi graphs necessarily do not contain any cycles of length four, so their girth must be six or more.
- Bipartite dimension, the minimum number of complete bipartite graphs whose union is the given graph
- Bipartite double cover, a way of transforming any graph into a bipartite graph by doubling its vertices
- Bipartite hypergraph, a generalization of bipartiteness to hypergraphs.
- Bipartite matroid, a class of matroids that includes the graphic matroids of bipartite graphs
- Bipartite network projection, a weighting technique for compressing information about bipartite networks
- Convex bipartite graph, a bipartite graph whose vertices can be ordered so that the vertex neighborhoods are contiguous
- Multipartite graph, a generalization of bipartite graphs to more than two subsets of vertices
- Parity graph, a generalization of bipartite graphs in which every two induced paths between the same two points have the same parity
- Quasi-bipartite graph, a type of Steiner tree problem instance in which the terminals form an independent set, allowing approximation algorithms that generalize those for bipartite graphs
- Split graph, a graph in which the vertices can be partitioned into two subsets, one of which is independent and the other of which is a clique
- Zarankiewicz problem on the maximum number of edges in a bipartite graph with forbidden subgraphs
Credits to the contents of this page go to the authors of the corresponding Wikipedia page: en.wikipedia.org/wiki/Bipartite graph.