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Concept
Hypergraph
Summary
In mathematics, a hypergraph is a generalization of a graph in which an edge can join any number of vertices. In contrast, in an ordinary graph, an edge connects exactly two vertices.
Formally, a directed hypergraph is a pair , where is a set of elements called nodes, vertices, points, or elements and is a set of pairs of subsets of . Each of these pairs is called an edge or hyperedge; the vertex subset is known as its tail or domain, and as its head or codomain.
The order of a hypergraph is the number of vertices in . The size of the hypergraph is the number of edges in . The order of an edge in a directed hypergraph is : that is, the number of vertices in its tail followed by the number of vertices in its head.
The definition above generalizes from a directed graph to a directed hypergraph by defining the head or tail of each edge as a set of vertices ( or ) rather than as a single vertex. A graph is then the special case where each of these sets contains only one element. Hence any standard graph theoretic concept that is independent of the edge orders will generalize to hypergraph theory.
Under one definition, an undirected hypergraph is a directed hypergraph which has a symmetric edge set: If then . For notational simplicity one can remove the "duplicate" hyperedges since the modifier "undirected" is precisely informing us that they exist: If then where means implicitly in.
While graph edges connect only 2 nodes, hyperedges connect an arbitrary number of nodes. However, it is often desirable to study hypergraphs where all hyperedges have the same cardinality; a k-uniform hypergraph is a hypergraph such that all its hyperedges have size k. (In other words, one such hypergraph is a collection of sets, each such set a hyperedge connecting k nodes.) So a 2-uniform hypergraph is a graph, a 3-uniform hypergraph is a collection of unordered triples, and so on. An undirected hypergraph is also called a set system or a family of sets drawn from the universal set.
Hypergraphs can be viewed as incidence structures.
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Ontological neighbourhood
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