Graphe biparti

In the mathematical field of graph theory, a bipartite graph (or bigraph) is a graph whose vertices can be divided into two disjoint and independent sets and , that is, every edge connects a vertex in to one in . Vertex sets and are usually called the parts of the graph. Equivalently, a bipartite graph is a graph that does not contain any odd-length cycles. The two sets and may be thought of as a coloring of the graph with two colors: if one colors all nodes in blue, and all nodes in red, each edge has endpoints of differing colors, as is required in the graph coloring problem. In contrast, such a coloring is impossible in the case of a non-bipartite graph, such as a triangle: after one node is colored blue and another red, the third vertex of the triangle is connected to vertices of both colors, preventing it from being assigned either color. One often writes to denote a bipartite graph whose partition has the parts and , with denoting the edges of the graph. If a bipartite graph is not connected, it may have more than one bipartition; in this case, the notation is helpful in specifying one particular bipartition that may be of importance in an application. If , that is, if the two subsets have equal cardinality, then is called a balanced bipartite graph. If all vertices on the same side of the bipartition have the same degree, then is called biregular. 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.

A Distributed Augmenting Path Approach for the Bottleneck Assignment Problem

Graph generative deep learning models with an application to circuit topologies

Non-planarity of Markoff graphs mod p

Graph generative deep learning models with an application to circuit topologies

A Distributed Augmenting Path Approach for the Bottleneck Assignment Problem

Non-planarity of Markoff graphs mod p