Tensor Product Graph Calculator

G × H tensor product

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About Tensor Product Graph Calculator

A tensor product graph calculator computing G×H: vertex set V(G)×V(H), (u₁,v₁)(u₂,v₂) adjacent iff u₁u₂∈E(G) AND v₁v₂∈E(H). |V|=|V₁|·|V₂|, |E|=2|E₁|·|E₂|. Hedetniemi's conjecture involves χ(G×H). Client-side.

Tensor Product Graph Calculator Features

  • G×H parameters
  • Edge formula
  • Hedetniemi
  • Bipartiteness
  • Connectivity
Tensor product G×H: (u₁,v₁)~(u₂,v₂) iff u₁u₂∈E(G) AND v₁v₂∈E(H). Both coordinates must move along edges simultaneously. |E(G×H)|=2|E₁|·|E₂|. Hedetniemi's conjecture (1966): χ(G×H) = min(χ(G),χ(H)).

How to Use

Select G and H:

  • |V|: |V₁|·|V₂|
  • |E|: 2|E₁|·|E₂|
  • χ: Hedetniemi bound

Hedetniemi's Conjecture

Conjectured (1966): χ(G×H) = min(χ(G),χ(H)). Proved for many cases. Shitov (2019) found a counterexample! But only for very large chromatic numbers. The conjecture holds for small χ and special graph families.

Properties

Commutative, associative. G×H may be disconnected even if G,H connected (iff both are bipartite or both non-bipartite). The adjacency matrix A(G×H) = A(G)⊗A(H) (Kronecker product).

Step-by-Step Instructions

  1. 1Select G, H.
  2. 2Compute G×H.
  3. 3Count vertices/edges.
  4. 4Check connectivity.
  5. 5Compare to Cartesian.

Tensor Product Graph Calculator — Frequently Asked Questions

How does this differ from Cartesian product?+

Cartesian: one coordinate changes. Tensor: both change simultaneously. Tensor product adjacency = Kronecker product of adjacency matrices. This gives 'diagonal' connections rather than 'grid' connections.

Was Hedetniemi's conjecture disproved?+

Yes! Yaroslav Shitov (2019) constructed a counterexample. But the graphs involved have enormous chromatic number. The conjecture still holds when min(χ(G),χ(H))≤4 and for many graph families.

When is G×H connected?+

G×H is connected iff G and H are connected and at least one is non-bipartite. If both are bipartite, G×H has exactly 2 components. This bipartiteness interaction is unique to the tensor product.

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