Dot Product Dimension Calculator

inner product embedding

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About Dot Product Dimension Calculator

A dot product dimension calculator computing ρ(G): minimum d such that vertices map to ℝ^d vectors where u~v iff ⟨f(u),f(v)⟩ ≥ 1. Dot product threshold graphs. ρ ≤ n. For bipartite: ρ ≤ n/2. Uses inner product geometry. Client-side.

Dot Product Dimension Calculator Features

  • ρ(G)
  • ⟨u,v⟩≥1
  • vs box/sph
  • Inner product
  • Common graphs
Dot product dimension ρ(G): minimum d for mapping V→ℝ^d where u~v iff ⟨f(u),f(v)⟩ ≥ 1. Inner product threshold representation. Threshold graphs: ρ=1. Trees: ρ≤3. Every graph: ρ ≤ n-1.

How to Use

Select graph:

  • ρ: Dot product dim
  • ⟨·,·⟩: Inner product
  • Threshold: ≥1

Representation

Each vertex v → vector f(v) in ℝ^d. u adjacent to v iff ⟨f(u), f(v)⟩ ≥ 1 (dot product at least 1). Captures angular proximity. Different from distance-based representations (boxicity, sphericity).

Bounds

Threshold graphs: ρ=1 (single dimension!). Trees: ρ≤3. Interval: ρ≤2. Planar: ρ = O(1) conjectured. General: ρ ≤ n-1. Computing ρ is NP-hard.

Step-by-Step Instructions

  1. 1Select graph.
  2. 2Compute ρ.
  3. 3Find embedding.
  4. 4Verify ⟨u,v⟩≥1.
  5. 5Compare dimensions.

Dot Product Dimension Calculator — Frequently Asked Questions

What makes dot product dimension different?+

Uses inner product (angle-based) instead of distance. Boxicity: L∞ distance. Sphericity: L₂ distance. Dot product: angular similarity. Different geometric perspectives on the same graph.

What are threshold graphs here?+

Threshold graphs have ρ=1: assignable weights so u~v iff w_u·w_v ≥ 1. Simplest dot-product graphs. Recognized in linear time. Very structured (nested neighborhoods).

Applications of dot product dimension?+

Recommendation systems: similarity via inner product. Machine learning: kernel methods. Social networks: influence propagation. Any setting where pairwise affinity determines connections.

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