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LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-26
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A large γ-self-regular set whose density lies between η and 1−η forces at least c∣W∣∣V(H)∣ induced copies of H

Statement

Fix a graph H with h=∣V(H)∣, a real 0<η<1/2, and constants γ(H,η),c(H,η)>0 and N(H,η) from the induced counting lemma. If 0<γ≤γ(H,η) and W⊆V(G) has ∣W∣≥N(H,η) with (W,W) γ-regular and η≤dG(W,W)≤1−η, then

ind⁡H(G)≥c(H,η)∣W∣h.

Facts & Assumptions

Given: A graph H with h vertices, a real 0<η<1/2, a graph G, a set W⊆V(G) with ∣W∣≥N(H,η), and a real 0<γ≤γ(H,η) such that (W,W) is γ-regular and η≤dG(W,W)≤1−η.

[L1]

If 0<η<1/2, the induced counting lemma supplies γ(H,η),c(H,η)>0 and N(H,η); it applies to h sets Wi of size at least N(H,η), repetitions allowed, when every relevant pair is γ(H,η)-regular and the edge- and nonedge-density bounds hold, and then yields at least c(H,η)∏i∣Wi∣ induced embeddings of H (Induced counting lemma: regular edge and nonedge pairs force many induced copies).

[L2]

If 0<γ≤γ(H,η) and (W,W) is γ-regular, then it is also γ(H,η)-regular: any A,B⊆W with ∣A∣≥γ(H,η)∣W∣ and ∣B∣≥γ(H,η)∣W∣ also satisfy the γ-threshold, so the defining density deviation is at most γ≤γ(H,η) (ϵ-regular pairs and self-regular vertex sets).

[L3]

Proof

technique · direct
1.1L1

Apply [L1] with W1=⋯=Wh=W. The repeated-set case is permitted by the statement of the counting lemma.

2.1step 1.1L2

By [L2], every pair (Wi,Wj) in this application is γ(H,η)-regular.

2.2step 1.1given

If ij is an edge of H, then the required density lower bound is the left inequality η≤dG(W,W). If ij is a non-edge, the required upper bound is the right inequality dG(W,W)≤1−η. So all density hypotheses of [L1] are satisfied.

3.1step 2.1step 2.2L1L3∎

Therefore [L1] produces at least c(H,η)∣W∣h induced embeddings of H in G, and [L3] identifies this number with ind⁡H(G).

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