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✓ 11 results · all verified · 7 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 4 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Sparse Restricted Subgraphs and the Rödl–Nikiforov Theorems — Examples

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-adaptedVerification: AI-generatedprecheck passaudited 2026-08-26Open item page →

The 0-sparse sets are exactly the stable sets and the 0-dense sets exactly the cliques

Example

For a nonempty set X, the condition of being 0-sparse is exactly that G[X] have no edges, and the condition of being 0-dense is exactly that G[X] be complete.

Facts & Assumptions

Given: A finite simple graph G and a nonempty set X⊆V(G).

[L1]

A set is 0-sparse when every vertex of it has at most 0⋅∣X∣=0 neighbours inside it (c-sparse, c-dense and c-restricted vertex sets).

[L2]

Stable sets and cliques are the edgeless and complete induced subgraphs, respectively (Cliques, stable sets, the clique number ω(G) and stability number α(G)).

Verification

technique · direct
1.1L1L2

By [L1], X is 0-sparse exactly when every vertex of X has no neighbour in X, which is exactly the statement that G[X] has no edges.

2.1step 1.1L2

Therefore [L2] identifies the 0-sparse sets with the stable sets.

3.1step 2.1L3∎

Applying [L3] to step 2.1 shows that the 0-dense sets are exactly the cliques.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

A clique of size s has self-density 1−1/s

Example

If X is a clique of size s≥1, then dG(X,X)=1−1/s.

Facts & Assumptions

Given: A clique X of size s in a finite simple graph G.

[L1]

The self-density is dG(X,X)=eG(X,X)/s2 (Edge counts and densities between nonempty vertex sets).

Verification

technique · direct
1.1given

Every ordered pair of distinct vertices of X is an edge, and the diagonal contributes nothing, so eG(X,X)=s(s−1).

2.1step 1.1L1algebra

Dividing by s2 as in [L1] gives dG(X,X)=s(s−1)/s2=1−1/s.

3.1step 2.1∎

In particular the self-density is always strictly less than 1, which is the reciprocal-size slack appearing in the dense half of A c-sparse set has self-density at most c, and a c-dense set has self-density at least 1−c−1/∣X∣.

ExampleConstruction: AI-adaptedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

In a disjoint union of cliques of order k the whole vertex set is (k/n)-sparse

Example

Let G be a nonempty disjoint union of cliques, each of order at most k, on a total of n≥1 vertices. Then V(G) is (k/n)-sparse.

Facts & Assumptions

Given: A nonempty graph G on n≥1 vertices whose connected components are cliques of order at most k.

[L1]
[L2]

A set is c-sparse when every vertex has at most c∣X∣ neighbours inside it (c-sparse, c-dense and c-restricted vertex sets).

Verification

technique · direct
1.1L1

By [L1], every vertex of G lies in a clique component of size at most k, so it has at most k−1≤k neighbours in the whole vertex set.

2.1step 1.1L2algebra∎

Since the whole set has size n, the bound of step 1.1 reads ∣NG(v)∣≤(k/n)n for every vertex v. Therefore [L2] makes V(G) (k/n)-sparse.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passaudited 2026-08-26Open item page →

For P3-free graphs Rödl's theorem holds with δ=ϵ, by an explicit argument

Example

If G is nonempty and P3-free and ϵ∈(0,12), then G contains an ϵ-restricted set of size at least ϵ∣V(G)∣.

Facts & Assumptions

Given: A real ϵ∈(0,12) and a nonempty P3-free graph G on n vertices.

[L2]

A clique is 0-dense, and if every vertex of the whole graph G has fewer than ϵn neighbours then V(G) is ϵ-sparse because ∣V(G)∣=n (c-sparse, c-dense and c-restricted vertex sets).

Verification

technique · direct
1.1L1

By [L1], every component of G is a clique.

2.1step 1.1L2

If some component has at least ϵn vertices, then that component is a clique and hence 0-dense by [L2], so it is an ϵ-restricted set of the required size.

2.2step 1.1L2

Otherwise every component has fewer than ϵn vertices, so every vertex has fewer than ϵn neighbours. Therefore the whole vertex set is ϵ-sparse by [L2].

3.1step 2.1step 2.2algebra∎

In either case G has an ϵ-restricted set of size at least ϵn.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passaudited 2026-08-26Open item page →

For 0≤ϵ<1/2, every sufficiently large ϵ-restricted set lies in one side

Example

Let G be the disjoint union of two cliques of the same order. For 0≤ϵ<1/2, every ϵ-restricted set of unbounded size is concentrated in one of the two cliques.

Facts & Assumptions

Given: A graph G that is the disjoint union of two cliques A and B of the same order, a real 0≤ϵ<1/2, and a nonempty set X⊆V(G) with a=∣X∩A∣ and b=∣X∩B∣.

[L2]

A nonempty set Y is ϵ-restricted when either every vertex of Y has at most ϵ∣Y∣ neighbours in Y, or every vertex of Y has at most ϵ∣Y∣ non-neighbours in Y other than itself (c-sparse, c-dense and c-restricted vertex sets).

Verification

technique · direct
1.1L1

If a=0 or b=0, then X lies in one clique, so [L1] makes it 0-dense and hence ϵ-restricted.

1.2given

Suppose a,b>0. The largest internal degree in X is max⁡{a,b}−1, while the largest number of non-neighbours in X is max⁡{a,b}, attained by a vertex in the smaller trace.

2.1step 1.2L2algebra

If X is ϵ-restricted, then [L2] and step 1.2 force either max⁡{a,b}−1≤ϵ(a+b) in the sparse case or max⁡{a,b}≤ϵ(a+b) in the dense case. Either implies 12(a+b)−1≤ϵ(a+b), so ∣X∣=a+b≤2/(1−2ϵ). Thus a restricted set meeting both sides has size bounded solely in terms of ϵ.

3.1step 1.1step 2.1∎

Therefore every sufficiently large ϵ-restricted set is concentrated on one side.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passaudited 2026-08-26Open item page →

The two sides of a balanced complete bipartite graph are large restricted sets

Example

In the balanced complete bipartite graph Km,m with m≥1, each side is 0-sparse and therefore restricted; a set taking linearly many vertices from both sides is not ϵ-restricted when 0≤ϵ<1/2.

Facts & Assumptions

Given: The complete bipartite graph Km,m with m≥1 and bipartition A⊔B, a real 0≤ϵ<1/2, and a set X meeting each side in exactly a vertices.

[L2]

A nonempty set Y is ϵ-restricted when either every vertex of Y has at most ϵ∣Y∣ neighbours in Y, or every vertex of Y has at most ϵ∣Y∣ non-neighbours in Y other than itself (c-sparse, c-dense and c-restricted vertex sets).

Verification

technique · direct
1.1L1

By [L1], each of A and B is 0-sparse, so each is a restricted set of size m.

1.2given

If X takes a vertices from each side, then every vertex of X has exactly a neighbours and a−1 non-neighbours inside X, while ∣X∣=2a.

2.1step 1.2L2algebra∎

For 0≤ϵ<1/2 and large a, neither inequality a≤ϵ(2a) nor a−1≤ϵ(2a) can hold. Hence such balanced mixed sets are not ϵ-restricted.

CounterexampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

A star has tiny self-density, yet no restricted subset containing its centre has more than two vertices

Statement refuted

Every weakly sparse set is sparse.

Facts & Assumptions

Given: A real ϵ<1/3, an integer m≥2, the star K1,m with centre v, and its full vertex set X.

[L1]

A set is ϵ-sparse or ϵ-dense according to the degree and non-neighbour bounds of c-sparse, c-dense and c-restricted vertex sets.

[L2]

The self-density is computed from the ordered internal edge count (Edge counts and densities between nonempty vertex sets).

Counterexample

technique · constructive
1.1L2givenconstruct

The set X has m+1 vertices and exactly m edges, so dG(X,X)=2m/(m+1)2, which tends to 0 as m→∞.

2.1step 1.1L1algebra

Let Y⊆X contain the centre v and at least two leaves. Then v has ∣Y∣−1 neighbours in Y, so the sparse inequality in [L1] fails when ϵ<1/3.

3.1step 2.1L1algebra

Each leaf of Y has at least ∣Y∣−2 non-neighbours in Y, so the dense inequality in [L1] also fails when ϵ<1/3. Thus no such Y is ϵ-restricted.

4.1step 1.1step 3.1discharge-construct∎

Hence a set can have arbitrarily small self-density without being sparse or dense in the maximum-degree sense.

CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

A subset of a c-sparse set that is not c-sparse

Statement refuted

Every subset of a c-sparse set is again c-sparse.

Facts & Assumptions

Given: An even integer 2m≥4, a perfect matching on 2m vertices, its whole vertex set X, and one matched edge {u,v}⊆X.

[L1]

A set is c-sparse when every vertex has at most c∣X∣ neighbours inside it (c-sparse, c-dense and c-restricted vertex sets).

Counterexample

technique · constructive
1.1L1givenconstruct

Every vertex of the matching has exactly one neighbour, so the whole set X is (1/(2m))-sparse by [L1].

2.1step 1.1L1algebra

The subset {u,v} has size 2, and each of its vertices still has one neighbour inside it. So it is not (1/(2m))-sparse whenever m≥2.

3.1step 2.1discharge-construct∎

Therefore sparsity does not pass to arbitrary subsets, which is exactly why A subset occupying at least a λ fraction of a c-sparse set is (c/λ)-sparse pays a factor of 1/λ.

CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

X can be c-sparse to Y while Y is not c-sparse to X

Statement refuted

If X is c-sparse to Y, then Y is c-sparse to X.

Facts & Assumptions

Given: A real c=12, a singleton X={x}, a set Y={y0,y1,y2}, and the graph with the unique edge xy0.

[L1]

The directional definition says that X is c-sparse to Y when every member of X has at most c∣Y∣ neighbours in Y, and similarly with the roles reversed (Sparsity of one vertex set to another, and weak sparsity of a pair).

Counterexample

technique · constructive
1.1L1givenconstruct

The vertex x has exactly one neighbour in Y, and 1≤12∣Y∣=32, so [L1] makes X 12-sparse to Y.

2.1step 1.1L1

The vertex y0 has one neighbour in X, but 12∣X∣=12, so [L1] shows that Y is not 12-sparse to X.

3.1step 2.1discharge-construct∎

Thus directional sparsity is not symmetric.

CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

The dense alternative in Rödl's theorem cannot be dropped

Statement refuted

The dense alternative in Rödl's theorem is unnecessary.

Facts & Assumptions

Given: A real 0≤ϵ<1/2 and the complete graph Kn with n≥2.

[L1]

A set is ϵ-sparse when each of its vertices has at most ϵ∣X∣ neighbours inside it (c-sparse, c-dense and c-restricted vertex sets).

Counterexample

technique · constructive
1.1givenconstruct

Every nonempty subset X of Kn with ∣X∣≥2 has each vertex adjacent to all ∣X∣−1 other vertices of X.

2.1step 1.1L1algebra

If such an X were ϵ-sparse, then [L1] would force (1−ϵ)∣X∣≤1. Hence every ϵ-sparse subset of Kn has size at most 1/(1−ϵ), a bound independent of n.

3.1step 2.1L2discharge-construct∎

The graph Kn is P3-free, since every three vertices induce a triangle rather than a path. For any proposed positive linear constant δ, choosing n>1/(δ(1−ϵ)) makes every ϵ-sparse set smaller than δn by step 2.1. Thus a linear restricted set in this P3-free family must use the dense alternative, which cannot be discarded.

False statementConstruction: AI-adaptedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26Open item page →

Every c-sparse set of size m contains a stable set of size at least m/2

Statement

Every c-sparse set of size m contains a stable set of size at least m/2.

Facts & Assumptions

Given: An integer n>8 divisible by 4, and a graph G that is the disjoint union of four cliques, each of order n/4.

Refutation

technique · direct
1.1L1

By [L1], the whole vertex set of G is a (1/4)-sparse set of size n.

1.2L2

By [L2], every stable set of G has size at most 4, because there are only four clique components.

2.1step 1.1step 1.2algebra∎

Since n>8, one has 4<n/2. So the sparse set of step 1.1 contains no stable set of size at least half its order. Therefore the claim is false.

Sources