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Width of a hypergraph

In graph theory, there are two related properties of a hypergraph that are called its "width".

Version
v1 · 2026-09-28 · History
Domain-specific #
12884
Domain group
Formal Sciences
Origin domain
Mathematics
Subdomains
Hypergraph Theory, Graph Theory → Mathematics

Core Idea

Width of a hypergraph is treated here as the recurring mathematicslogicstatistics identity summarized by this source-grounded definition: In graph theory, there are two related properties of a hypergraph that are called its "width". of the graph can be pinned by a single edge. Here, a matching is shown in red, and an edge that pins it in yellow. In graph theory, there are two related properties of a hypergraph that are called its "width". Given a hypergraph H = (V, E), we say that a set K of edges pins another set F of edges if.

Scope of Application

  • Documented setting. The width of a hypergraph is used in Hall-type theorems for hypergraphs.

  • Examples. Let H be the hypergraph with vertex set V = {A,B; a,b} and edge set: E = { {A,a}, {B,b}, {A,b}, {B,a} } The widths of H are.

  • Examples. w(H) = 2, since E is pinned e.g. by the set { {A,a}, {B,b} }, and cannot be pinned by any smaller set.

  • Examples. mw(H) = 1, since every matching can be pinned by a single edge.

  • Examples. There are two matchings: is pinned e.g. by { {A,b} }, and { {A,b}, {B,a} } is pinned e.g. by { {A, a} }.

Clarity

A clear use of Width of a hypergraph names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is In graph theory, there are two related properties of a hypergraph that are called its "width".

Manages Complexity

Width of a hypergraph compresses multiple mathematicslogicstatistics details into a stable diagnostic relation. The source shows both the central mechanism—mw(H) = 1, since every matching can be pinned by a single edge.—and the practical consequence—let H be the hypergraph with vertex set V = {A,B; a,b} and edge set: E = { {A,a}, {B,b}, {A,b}, {B,a} } The widths of H are.

Abstract Reasoning

  1. Type the carrier. Identify the mathematicslogicstatistics entities to which the claim applies.
  2. State the relation. Use the source-grounded identity: In graph theory, there are two related properties of a hypergraph that are called its "width".
  3. Check operation and conditions. There are two matchings: is pinned e.g. by { {A,b} }, and { {A,b}, {B,a} } is pinned e.g. by { {A, a} }.
  4. Demand recognition evidence.

Knowledge Transfer

Within the home domain. Knowledge about Width of a hypergraph transfers literally when a new case preserves the same carrier type, relation, and recognition test. The width of a hypergraph is used in Hall-type theorems for hypergraphs. Let H be the hypergraph with vertex set V = {A,B; a,b} and edge set: E = { {A,a}, {B,b}, {A,b}, {B,a} } The widths of H are. Beyond the home domain. No canonical parent is asserted for Width of a hypergraph.

Relationships to Other Abstractions

Local relationship map for Width of a hypergraphParents appear above the current abstraction, mutual partners to the right, and children below. Node labels state whether each abstraction is prime or domain-specific; colors identify relation types.Width of a hypergraphDOMAINDomain-specific abstraction: Graph Invariant — is a kind of, conditionalGraph InvariantDOMAIN

Current abstraction Width of a hypergraph Domain-specific

Parents (1) — more general patterns this builds on

  • Width of a hypergraph is a kind of, conditional Graph Invariant Domain-specific

    Supported only after specifying which width definition and hypergraph category is intended; the live name covers two related parameters.

    Condition / exception Supported only after specifying which width definition and hypergraph category is intended; the live name covers two related parameters.

Hierarchy path (1) — routes to 1 parentless root

Neighborhood in Abstraction Space

Width of a hypergraph sits in a moderately populated region (47th percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.

Family — Combinatorial Optimization & Discrete Structures (31 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-10-08