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Sparse Restricted Subgraphs and the Rödl–Nikiforov Theorems
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Countability and Uncountability
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability Spaces and Random Variables
- Foundations of the Real Numbers for Analysis
- Graph Colouring
- Graphs, Walks and Connectivity
- Inclusion–Exclusion, the Pigeonhole Principle and Double Counting
- Induced Subgraphs and Hereditary Graph Classes
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Order, Zorn's Lemma, and the Axiom of Choice
- Power Series and Real-Analytic Functions
- Properties of the Integral and the Working FTC
- Regular Pairs and Induced Counting
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Logarithm and General Powers
- The Riemann Integral: Definition and Integrability
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
The input from the regular-pairs page is the whole quantitative engine here: edge density, -regular pairs, typical-degree estimates, the induced counting lemma, and the theorem producing large self-regular subsets. Those results let the page translate between maximum-degree sparsity and density sparsity without redoing regularity from scratch. The induced-copy number is the bridge from counting information to structure, and the complement dictionary is what lets sparse and dense conclusions be treated in parallel rather than separately.
The page defines -sparse, -dense, and -restricted sets, together with the directional and weak density language used in the literature. It proves the transfer, complement, and trimming lemmas that convert a self-regular set of extreme density into a genuinely restricted set, then proves Nikiforov's few-copies theorem and Rödl's theorem. The closing results compare the density and maximum-degree normalisations, extend Rödl to forbidden families and large induced subgraphs, and show that boundedly many extreme-self-density parts suffice to cover or partition every -free graph.
3 · Logical flowchart
4 · Definitions, theorems and proofs
-sparse, -dense and -restricted vertex sets
Definition
Let be a finite simple graph and let be real. A nonempty vertex set is -sparse when
for every , and it is -dense when
for every . Thus -dense means that every vertex of has at most non-neighbours inside other than itself.
A set is -restricted when it is -sparse or -dense. The condition is internal to the induced subgraph (Subgraphs, induced subgraphs and spanning subgraphs), and the dense clause is the sparse clause read in the complement.
Remarks
This page keeps the source's maximum-degree normalisation. The edge-density normalisation appears separately in Sparsity of one vertex set to another, and weak sparsity of a pair and is compared to the present one by the lemmas immediately following this definition.
Sparsity of one vertex set to another, and weak sparsity of a pair
Definition
Let be a finite simple graph and let .
- For disjoint nonempty sets , is -sparse to when every has at most neighbours in .
- For nonempty sets , the ordered pair is weakly -sparse when , with the ordered edge count of Edge counts and densities between nonempty vertex sets.
- Such a pair is weakly -dense when it is weakly -sparse in the complement graph.
The directional notion need not be symmetric, while the weak notion is an edge-count and so is symmetric. For nonempty , taking makes weak sparsity the self-density condition discussed in Edge counts and densities between nonempty vertex sets.
For disjoint nonempty vertex sets, weak -sparsity says exactly that the edge density is at most
Statement
Let be a finite simple graph, let , and let be disjoint nonempty sets. Then is weakly -sparse if and only if .
Facts & Assumptions
Given: A finite simple graph , a real , and disjoint nonempty sets .
Weak -sparsity means (Sparsity of one vertex set to another, and weak sparsity of a pair).
The edge density is , where counts ordered pairs that form an edge (Edge counts and densities between nonempty vertex sets).
Because and are disjoint, each edge between them contributes exactly one such ordered pair, so (Edges between disjoint vertex sets; complete, anticomplete, pure and mixed pairs).
Proof
By [L3], the inequality of [L1] is the same as .
Since and are nonempty, dividing by is legitimate, and [L2] turns the inequality of step 1.1 into .
Reversing the same algebra shows the converse implication, so the two conditions are equivalent.
A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size
Statement
Let be a finite simple graph, let , and let be nonempty. Then is -sparse in if and only if every vertex of the induced subgraph has degree at most . In particular, if , then is -sparse in if and only if it is -sparse in .
Facts & Assumptions
Given: A finite simple graph , a real , and a nonempty set .
The set is -sparse when for every (-sparse, -dense and -restricted vertex sets).
In the induced subgraph , the neighbours of a vertex are exactly the vertices of (Subgraphs, induced subgraphs and spanning subgraphs, Adjacency, incidence, open and closed neighbourhoods, vertex degree, minimum degree and maximum degree).
Proof
By [L2], for each the degree of in is exactly .
Therefore the inequalities in [L1] are exactly the degree bounds in the induced subgraph.
The same identity of neighbourhoods holds in any larger induced subgraph containing , so the ambient graph is irrelevant once the vertex set is fixed.
A -sparse set has self-density at most , and a -dense set has self-density at least
Statement
Let be a finite simple graph, let , and let be nonempty.
- If is -sparse, then .
- If is -dense, then .
Facts & Assumptions
Given: A finite simple graph , a real , and a nonempty set .
A set is -sparse exactly when every vertex of the induced graph has degree at most (A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size, -sparse, -dense and -restricted vertex sets).
The self-density is (Edge counts and densities between nonempty vertex sets).
The ordered edge count satisfies (Double counting: for a relation between finite sets, The sum over a finite index set, and its product form).
Proof
If is -sparse, then [L1] bounds every summand in [L3] by , so .
Dividing the inequality of step 1.1 by and using [L2] gives .
If is -dense, then every vertex of has at most non-neighbours in , so it has at least neighbours in . Summing as in [L3] gives , and [L2] turns this into .
A set of self-density at most has a subset of at least half its size that is -sparse
Statement
Let be a finite simple graph, let , and let be nonempty. If , then there is a subset with such that is -sparse.
Facts & Assumptions
Given: A finite simple graph , a real , and a nonempty set with .
The ordered internal edge count is the sum of the internal degrees: (Double counting: for a relation between finite sets, The sum over a finite index set, and its product form).
The self-density inequality is equivalent to (Edge counts and densities between nonempty vertex sets).
A set is -sparse exactly when every vertex of the induced graph on it has degree at most times its size (A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size, -sparse, -dense and -restricted vertex sets).
Proof
By [L1] and [L2], the average internal degree of a vertex of is at most .
Let . If , then the sum of the nonnegative internal degrees would be strictly larger than , contradicting step 1.1. Hence has , and every has .
For one has , because . Thus [L3] makes -sparse.
A set is -sparse in exactly when it is -dense in , so -restrictedness is complement-invariant
Statement
Let be a finite simple graph, let , and let be nonempty. Then is -sparse in if and only if is -dense in . Consequently is -restricted in if and only if it is -restricted in .
Facts & Assumptions
Given: A finite simple graph , a real , and a nonempty set .
For distinct vertices , they are adjacent in exactly when they are nonadjacent in (Graph isomorphisms, automorphisms and graph complements, Adjacency, incidence, open and closed neighbourhoods, vertex degree, minimum degree and maximum degree).
The definitions of -sparse, -dense, and -restricted are those of -sparse, -dense and -restricted vertex sets.
Proof
For , the set is exactly by [L1].
Therefore the inequality defining -sparsity in is exactly the inequality defining -density in , and vice versa, by [L2].
Since -restricted means the disjunction of the sparse and dense conditions, step 2.1 shows that restrictedness is unchanged by complementation.
A subset occupying at least a fraction of a -sparse set is -sparse
Statement
Let be a finite simple graph, let , let , and let be nonempty. If is -sparse and , then is -sparse.
Facts & Assumptions
Given: A finite simple graph , reals and , and nonempty sets such that is -sparse and .
In the induced subgraph on a set, sparsity is exactly a maximum-degree bound (A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size, -sparse, -dense and -restricted vertex sets).
Proof
For every , the degree of in is at most its degree in .
Since is -sparse, [L1] gives ; and because , one has . Hence for every .
Applying [L1] again shows that is -sparse.
Sparsity is preserved when the parameter grows, and every nonempty set of at most two vertices is -restricted
Statement
Let be a finite simple graph and let .
- If a nonempty set is -sparse, then it is -sparse.
- If is nonempty and , then is -restricted.
Facts & Assumptions
Given: A finite simple graph , reals , and a nonempty set .
A nonempty set is -sparse when every vertex of has at most neighbours in ; it is -dense when every vertex has at most non-neighbours in other than itself; and it is -restricted when it is -sparse or -dense (-sparse, -dense and -restricted vertex sets).
Proof
If is -sparse, then every vertex of has at most neighbours in , so [L1] makes -sparse.
If , then its only vertex has no neighbour and no non-neighbour inside , so [L1] makes both -sparse and -dense.
If , then either the two vertices are adjacent or they are not. In the first case is -dense, and in the second it is -sparse. So [L1] makes every two-element set -restricted.
A -sparse set satisfies , and a -dense set satisfies
Statement
Let be a finite simple graph, let , and let be nonempty.
- If is -sparse, then .
- If is -dense, then .
Facts & Assumptions
Given: A finite simple graph , a real , and a nonempty set .
If is -sparse, then every vertex of has degree at most (A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size, -sparse, -dense and -restricted vertex sets).
A graph of maximum degree at most has chromatic number at most (The greedy colouring bound for every nonnull finite graph, Proper vertex colourings and chromatic number).
Every finite graph satisfies (The bounds and , Cliques, independent sets, clique number and independence number).
The published definitions Cliques, independent sets, clique number and independence number and Cliques, stable sets, the clique number and stability number define the same invariants and under the same symbols.
In the complement graph, stable sets become cliques and sparse sets become dense sets by Complementation swaps cliques with stable sets, so and A set is -sparse in exactly when it is -dense in , so -restrictedness is complement-invariant.
Proof
In the sparse case, [L1] gives , so [L2] gives .
The two published definitions of and agree by [L4], so the complement statement can be read with the same symbols.
Applying [L3] to yields , hence .
If is -dense, then [L5] makes -sparse in , so step 2.1 applied there gives a stable set of size at least . Reading that set back in via [L5] gives a clique of the same size.
An -regular pair is -regular for every with
Statement
Let . If is an -regular pair, then it is an -regular pair.
Facts & Assumptions
Given: Reals , disjoint nonempty vertex sets , and an -regular pair .
An -regular pair requires the density deviation bound for all subsets , with and (-regular pairs and self-regular vertex sets, Edge counts and densities between nonempty vertex sets).
Proof
If and , then also and because .
The -regularity of therefore gives . This is exactly the definition of -regularity.
Deleting the high-degree vertices of a -self-regular set of density leaves more than of it, and that remainder is -sparse
Statement
Let , let be nonempty, and suppose is a -regular pair of density . Then there is a subset with such that is -sparse.
Facts & Assumptions
Given: A finite simple graph , a real , a nonempty set , and a density such that is -regular.
If is a -regular pair of density and a fixed set has , then fewer than vertices of have more than neighbours in (In a regular pair, fewer than vertices have too small a degree into a large subset, and fewer than have too large a degree, -regular pairs and self-regular vertex sets).
A set is -sparse exactly when every vertex of the induced graph on it has degree at most times its size (A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size, -sparse, -dense and -restricted vertex sets).
Proof
Apply [L1] to the pair with . Since , fewer than vertices of have more than neighbours in .
Let be the remaining vertices. Then .
For every , one has . Therefore [L2] makes -sparse.
Deleting the low-degree vertices of a -self-regular set of density leaves more than of it, and that remainder is -dense
Statement
Let , let be nonempty, and suppose is a -regular pair of density . Then there is a subset with such that is -dense.
Facts & Assumptions
Given: A finite simple graph , a real , a nonempty set , and a density such that is -regular.
In a -regular pair , all but fewer than vertices of have at least neighbours in any subset of size at least (In a regular pair, fewer than vertices have too small a degree into a large subset, and fewer than have too large a degree, -regular pairs and self-regular vertex sets).
A set is -dense exactly when every vertex has at most non-neighbours inside it other than itself (-sparse, -dense and -restricted vertex sets).
Proof
Apply [L1] to the pair with . Fewer than vertices of then have fewer than neighbours in .
Let be the remaining vertices. Then .
For , at most of its neighbours lie outside , so has at least neighbours in .
Hence has at most non-neighbours inside . By [L2], the set is -dense.
A large -self-regular set whose density lies between and forces at least induced copies of
Statement
Fix a graph with , a real , and constants and from the induced counting lemma. If and has with -regular and , then
Facts & Assumptions
Given: A graph with vertices, a real , a graph , a set with , and a real such that is -regular and .
If , the induced counting lemma supplies and ; it applies to sets of size at least , repetitions allowed, when every relevant pair is -regular and the edge- and nonedge-density bounds hold, and then yields at least induced embeddings of (Induced counting lemma: regular edge and nonedge pairs force many induced copies).
If and is -regular, then it is also -regular: any with and also satisfy the -threshold, so the defining density deviation is at most (-regular pairs and self-regular vertex sets).
The induced-copy number counts induced embeddings of (The induced-embedding count , Induced embeddings and induced copies of a graph).
Proof
Apply [L1] with . The repeated-set case is permitted by the statement of the counting lemma.
By [L2], every pair in this application is -regular.
If is an edge of , then the required density lower bound is the left inequality . If is a non-edge, the required upper bound is the right inequality . So all density hypotheses of [L1] are satisfied.
Therefore [L1] produces at least induced embeddings of in , and [L3] identifies this number with .
If has fewer than induced copies of and , then has fewer than
Statement
Let have vertices. If has vertices, , and satisfies , then
Facts & Assumptions
Given: A graph with vertices, a graph on vertices, reals and , and a subset with and .
An induced embedding of into is, by definition, an induced embedding of into whose image lies in (Induced embeddings and induced copies of a graph, Subgraphs, induced subgraphs and spanning subgraphs).
The induced-copy number counts induced embeddings (The induced-embedding count ).
Proof
By [L1], every induced embedding counted by is also counted by .
Therefore by [L2].
Since , one has . Substituting this into step 2.1 yields .
Nikiforov: for every and every there is such that every graph with has an -restricted vertex set of size at least
Statement
Fix a graph with and a real . Then there exists such that every nonempty finite simple graph with
has an -restricted vertex set of size at least .
Facts & Assumptions
Given: A graph with vertices and a real .
For each , the self-regular-subset theorem gives a constant such that every nonempty graph on vertices has a subset with and -regular (Every finite graph has a linearly large -self-regular vertex subset, -regular pairs and self-regular vertex sets).
The induced counting constants include , and if , , is -regular, and its self-density lies between and , then (A large -self-regular set whose density lies between and forces at least induced copies of ).
A low-density -regular set has a large sparse subset, and a high-density one has a large dense subset, with the explicit parameters supplied by Deleting the high-degree vertices of a -self-regular set of density leaves more than of it, and that remainder is -sparse and Deleting the low-degree vertices of a -self-regular set of density leaves more than of it, and that remainder is -dense.
Every nonempty set of at most two vertices is -restricted, hence -restricted (Sparsity is preserved when the parameter grows, and every nonempty set of at most two vertices is -restricted).
Proof
Set and choose so small that and from [L2]. Then , and the dense trimming parameter from [L3] is also at most , because , , and .
If , the induced-copy hypothesis is never satisfied: both sides of its displayed inequality are . Thus suppose . Let be the constant of [L1], let and be the constants of [L2], put , and set Then and depends only on and .
Now let be a nonempty graph on vertices with . If , then any singleton is -restricted by [L4] and satisfies , because . Hence suppose . By [L1] choose with such that is -regular.
If , then [L2] gives , contrary to the hypothesis on . Therefore either or .
In the first case, the low-density trimming lemma in [L3] yields a subset with that is -sparse by step 1.1. In the second case, the high-density trimming lemma in [L3] yields a subset with the same size bound that is -dense. In either case is -restricted.
Step 3.1 handles small , and steps 4.1 and 5.1 handle all remaining cases, so every graph satisfying the induced-copy bound has an -restricted set of size at least .
Rödl: for every and every there is such that every nonempty -free graph has an -restricted vertex set of size at least
Statement
For every graph and every there exists such that every nonempty -free finite simple graph has an -restricted vertex set of size at least .
Facts & Assumptions
Given: A graph and a real .
If is -free, then (-free and -free graphs under the induced-subgraph convention, The induced-embedding count ).
For every graph and there is such that every nonempty graph satisfying has an -restricted set of size at least (Nikiforov: for every and every there is such that every graph with has an -restricted vertex set of size at least ).
Proof
Let be the constant supplied by [L2] for the given and .
If is nonempty and -free, then [L1] gives .
Applying [L2] to step 1.2 yields the desired -restricted set.
Rödl's theorem for a nonempty family of forbidden induced subgraphs
Statement
Let be a nonempty family of graphs and let . Then there exists such that every -free nonempty finite simple graph has an -restricted vertex set of size at least .
Facts & Assumptions
Given: A nonempty family of graphs and a real .
A graph that is -free is -free for every (-free and -free graphs under the induced-subgraph convention).
For every graph and there is such that every nonempty -free graph has an -restricted set of size at least (Rödl: for every and every there is such that every nonempty -free graph has an -restricted vertex set of size at least ).
Proof
Choose any graph .
By [L2], there is a constant such that every nonempty -free graph has an -restricted set of size at least .
If is nonempty and -free, then [L1] makes it -free, so step 2.1 applies to .
The edge-density form of Rödl's theorem: every nonempty -free graph has a linearly large set of self-density at most or at least
Statement
For every graph and every there exists such that every nonempty -free finite simple graph contains a set with and either or .
Facts & Assumptions
Given: A graph and a real .
Rödl's theorem supplies a constant such that every nonempty -free graph has an -restricted set of size at least (Rödl: for every and every there is such that every nonempty -free graph has an -restricted vertex set of size at least ).
A -sparse set has self-density at most , while a -dense set has self-density at least (A -sparse set has self-density at most , and a -dense set has self-density at least ).
Proof
Let be the constant from [L1] for the parameter , and set .
If is nonempty and -free, then [L1] gives a set with that is -restricted.
If is -sparse, then [L2] gives .
If is -dense and , then [L2] gives . If instead , then by step 2.1, and the single-vertex set has self-density and size by the choice of in step 1.1.
In every case there is a set of size at least whose self-density is at most or at least .
The edge-density form of Rödl's theorem implies the maximum-degree form, with and each shrunk by a constant factor
Statement
Assume the edge-density form of Rödl's theorem holds at parameter with constant . Then the maximum-degree form holds at parameter with constant .
Facts & Assumptions
Given: A graph , a real , and the edge-density form of Rödl's theorem at parameter with constant .
The edge-density form supplies, in every nonempty -free graph , a set of size at least with or (The edge-density form of Rödl's theorem: every nonempty -free graph has a linearly large set of self-density at most or at least ).
If , then some subset with is -sparse (A set of self-density at most has a subset of at least half its size that is -sparse).
A set is -dense in exactly when it is -sparse in (A set is -sparse in exactly when it is -dense in , so -restrictedness is complement-invariant).
Proof
Let be a nonempty -free graph. By [L1], choose with and either or .
In the sparse branch, [L2] applied with gives a subset with that is -sparse, hence -restricted.
In the dense branch, the diagonal convention gives ; applying [L2] to yields a subset with that is -sparse in , and [L3] turns this into -dense, hence -restricted, in .
In either branch , so the maximum-degree form holds with constant .
A linearly large induced subgraph of a graph with few induced copies again has a linearly large restricted set
Statement
Fix a graph , a real , and a fraction . Then there exists such that whenever is a nonempty graph on vertices with and satisfies , the induced subgraph contains an -restricted set of size at least .
Facts & Assumptions
Given: A graph , a real , and a real .
If has vertices, , , and , then (If has fewer than induced copies of and , then has fewer than ).
There is such that every nonempty graph with has an -restricted set of size at least (Nikiforov: for every and every there is such that every graph with has an -restricted vertex set of size at least ).
Proof
Let be the constant of [L2] for and , and set . Then , , and .
If and , then [L1] gives .
Applying [L2] inside yields an -restricted set of size at least . Since by step 1.1, this is the required set.
For every a bounded number of disjoint -restricted sets covers all but vertices of an -free graph
Statement
Fix a graph , a real , and . Then there is an integer such that every nonempty -free finite simple graph contains pairwise disjoint -restricted sets for some , whose union misses fewer than vertices.
Facts & Assumptions
Given: A graph , reals and .
Every induced subgraph of an -free graph is -free (Every class defined by forbidden induced subgraphs is hereditary, Every induced subgraph of an -free graph is -free, -free and -free graphs under the induced-subgraph convention).
Rödl's theorem provides a constant such that every nonempty -free graph contains an -restricted set of size at least times its order (Rödl: for every and every there is such that every nonempty -free graph has an -restricted vertex set of size at least ).
Sparsity is internal to induced subgraphs, so a set that is -restricted in an induced subgraph is also -restricted in the ambient graph (A set is -sparse exactly when the maximum degree of the graph it induces is at most times its size, -sparse, -dense and -restricted vertex sets).
Proof
Let be the constant from [L2]. Choose minimal with .
Starting from , repeatedly apply [L2] to the induced subgraph on the current remainder while that remainder has at least vertices. By [L1] each remainder is still -free, so this produces pairwise disjoint -restricted sets .
After each extraction, at least a fraction of the current remainder is removed. Hence after steps the remainder has size at most . In particular, after steps it has size strictly below by the choice of .
The process therefore stops after some number of extractions, and [L3] makes every extracted set -restricted in the original graph.
Every -free graph partitions into boundedly many vertex sets of self-density at most or at least
Statement
Fix a graph and a real . Then there exists an integer such that every -free finite simple graph admits a partition
in which every part satisfies or .
Facts & Assumptions
Given: A graph and a real .
The edge-density form of Rödl's theorem supplies a constant such that every nonempty -free finite simple graph contains a set with and either or (The edge-density form of Rödl's theorem: every nonempty -free graph has a linearly large set of self-density at most or at least ).
Every induced subgraph of an -free graph is again -free (Every class defined by forbidden induced subgraphs is hereditary, Every induced subgraph of an -free graph is -free, -free and -free graphs under the induced-subgraph convention).
If then has the same adjacencies on every subset as does, so (Subgraphs, induced subgraphs and spanning subgraphs, Edge counts and densities between nonempty vertex sets).
For , the sequence tends to (For the sequence is null, and for the sequence diverges to ).
Proof
Let be the constant from [L1], set , and note that . Then every nonempty -free graph contains a set with and either or .
Since , [L3] applied to yields a natural number with .
Let be an -free finite simple graph. If , the empty partition works, so assume . Put . For each , if stop; otherwise the induced subgraph is -free by [L2], so step 1.1 and [F1] give a nonempty set with and either or . Define .
If the process stops at some stage because , then the nonempty extracted sets partition and each already has self-density at most or at least , hence in particular at most or at least .
Assume now that the process does not stop before stage . Then are pairwise disjoint nonempty sets, and an induction on using step 2.2 gives for every . Hence by step 2.1, while by step 2.2.
Put and for . Then partition . Writing , , and , step 3.2 gives .
If , then because the new ordered edges are those incident with at least one vertex of . Therefore .
If instead , then . Since and , one has , so .
In the situation of steps 3.2 and 4.1, step 5.1 or 5.2 handles , while each for already has self-density at most or at least . Therefore every part of the partition has self-density at most or at least .
Step 3.1 settles the case where the extraction stops early, and step 6.1 settles the case where it reaches stage . So works for every and .
Bounded degree against bounded density: the two statements of Rödl's theorem, and which one is stronger
The maximum-degree form is stronger than the density form. If every vertex of has at most neighbours, then the self-density is at most by A -sparse set has self-density at most , and a -dense set has self-density at least . The converse fails: small average degree does not control exceptional vertices, and a large star is the basic witness.
What this page proves is the stronger form Rödl: for every and every there is such that every nonempty -free graph has an -restricted vertex set of size at least , then derives the density form The edge-density form of Rödl's theorem: every nonempty -free graph has a linearly large set of self-density at most or at least , and finally shows by The edge-density form of Rödl's theorem implies the maximum-degree form, with and each shrunk by a constant factor that the weaker statement implies the stronger one after shrinking the constants by a fixed factor.
Why the self-density bound for a dense set carries a slack
The self-density counts ordered adjacent pairs of distinct vertices and divides by , so the diagonal pairs never contribute. A clique on vertices therefore has self-density , not . That is why the dense conclusion in A -sparse set has self-density at most , and a -dense set has self-density at least carries the reciprocal-size term. The density form of Rödl's theorem absorbs this term by changing its constants and states the cleaner threshold .
What this proof gives for , and why the regularity route is expensive
The proof of Rödl: for every and every there is such that every nonempty -free graph has an -restricted vertex set of size at least runs through the self-regular subset theorem and hence through Szemerédi regularity. Its constant is therefore extremely small: it is assembled from the regularity output, the counting-lemma constant, and the trimming loss in Nikiforov: for every and every there is such that every graph with has an -restricted vertex set of size at least . Nothing on this page claims that this bound is close to optimal.
The significance of the theorem is structural, not quantitative: every -free graph contains a linearly large region that is sparse or dense in a strong sense. Later work improves the constants by avoiding the full regularity machinery; this page does not.
5 · Examples, counterexamples and false statements
None yet.
Sources
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