Alphabeta Math
CorollaryStatement: Literature-sourcedProof: AI-generatedprecheck passaudited 2026-08-31
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Few induced copies force a linearly large induced subgraph with bounded maximum degree

Statement

Let H be a finite graph and let ϵ(0,12). Then there exists δ>0 such that every nonempty finite graph G with

indH(G)<(δV(G))V(H)

has a set XV(G) with XδV(G) for which one of G[X] or G[X] has maximum degree at most ϵX.

Facts & Assumptions

Given: A finite graph H, a real ϵ(0,12), and a nonempty finite graph G with indH(G)<(δV(G))V(H).

[L2]

In a sparse graph, any prescribed size up to half the order can be chosen so that the induced subgraph has proportionally bounded maximum degree (A sparse graph has a prescribed-size induced subgraph of bounded maximum degree).

Proof

technique · direct
1.1

Apply [L1] with parameter ϵ/4 and let δ:=δ0/2. Then there is a set ZV(G) with Z2δV(G) such that either G[Z] or G[Z] is (ϵ/4)-sparse.

L1choosegiven
2.1

Let m:=δV(G). Since Z2δV(G), we have m(Z+1)/2. Applying [L2] inside the sparse side on Z gives XZ with X=mδV(G) such that the same side has maximum degree at most 4(ϵ/4)(m1)ϵX.

step 1.1L2algebra
3.1

Thus one of G[X] or G[X] has maximum degree at most ϵX, as required.

step 2.1

Depends on

Used by

Dependency tree · two levels

20 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources