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Iterative Restriction and Comb-Extraction Lemmas
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Blockades, Combs and Pattern Graphs
- 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 and the Probabilistic Method
- 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
- Iterative Sparsification and the Five-Vertex Path
- 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
- Polynomial Rödl, Virality and Erdős–Hajnal Equivalence
- 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
- Sparse Restricted Subgraphs and the Rödl–Nikiforov Theorems
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Erdős–Hajnal Property and Homogeneous Sets
- The Exponential Function
- The Five-Cycle and the Erdős-Hajnal Property
- The Logarithm and General Powers
- The Riemann Integral: Definition and Integrability
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Trees, Forests and Spanning Trees
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
This page isolates the reusable Section 2 lemmas that sit between the earlier blockade machinery and the later six-vertex structure pages. It keeps the graph-class-free parts of the iteration visible: the nearly covered sparse-pair extraction, the leaf-reducible reduction, the multiplicative sparsity-drop lemma, and the comb-producing alternative.
The proofs are written in the normalization the later route actually uses. In particular, the generalized nearly covered sparse-pair lemma removes the unused -free hypothesis from Claim 5.2.1, and the leaf-reducible lemma keeps only the graph-level consequence needed for later pages.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Leaf-reducible finite graph families
Definition
Let be a finite family of finite graphs. We say that is leaf-reducible if there exist a graph and a leaf such that the modified family
has the Erdős-Hajnal property in the family sense (The Erdős–Hajnal property and an Erdős–Hajnal constant for a hereditary graph class, -free and -free graphs under the induced-subgraph convention).
Thus a leaf-reducible family is one for which deleting one leaf from one member produces a new forbidden family already known to have the Erdős-Hajnal property.
A sparse graph without a large sparse pair has a large nearly covered sparse pair
Statement
Let with , and let be a -sparse graph with . Suppose that is not -sparse, and that there do not exist disjoint sets such that
and is -sparse to . Then there exist a vertex and disjoint sets such that:
- and ;
- and ;
- is -sparse to ; and
- every vertex of has at least neighbours in .
Facts & Assumptions
Given: Parameters and a graph satisfying the displayed hypotheses.
A graph is -sparse when every vertex has degree at most (-sparse, -dense and -restricted vertex sets, Adjacency, incidence, open and closed neighbourhoods, vertex degree, minimum degree and maximum degree).
The assertion that is -sparse to means that every vertex of has at most neighbours in (Sparsity of one vertex set to another, and weak sparsity of a pair).
Proof
Because is not -sparse, some vertex has degree at least . Let . Then .
Let be the set of vertices in with at least neighbours in . Averaging over the edges between and , some vertex of has at least neighbours in . Since every vertex has degree at most by [L1], we obtain , so .
Define . Since is -sparse, [L1] gives , and because we have . Therefore . Also, every vertex of has fewer than neighbours in by definition of .
Let be the set of vertices with at most neighbours in , and put . The number of edges between and is at most , so at most vertices of have more than neighbours in . Hence at least vertices of have at most neighbours in .
If , then step 4.1 gives a set with such that every vertex of has at most neighbours in . By [L2], the pair is then a forbidden large sparse pair, contradicting the hypothesis. Therefore .
Since by step 1.1 and by step 5.1, we have . By definition of , every vertex of has more than neighbours in . Also step 3.1 gives at most neighbours in for each vertex of , while step 5.1 implies ; hence , so every vertex of has at most neighbours in . Therefore is -sparse to .
Step 3.1 gives , step 1.1 gives , and steps 3.1 and 6.1 give the size, sparsity, and neighbourhood clauses. These are exactly the four clauses of the statement.
Leaf-reducible families yield a large anticomplete pair or a deeper restricted induced subgraph
Statement
Let be a leaf-reducible finite family of graphs. Then there exist constants and such that for every , every , and every -sparse -free graph , at least one of the following holds:
- there are disjoint sets with and anticomplete to ; or
- has a -restricted induced subgraph with at least vertices.
Facts & Assumptions
Given: A leaf-reducible finite family , parameters and , and a -sparse -free graph .
Because is leaf-reducible, there exist and a leaf such that has the Erdős-Hajnal property (Leaf-reducible finite graph families).
For a finite family, the Erdős-Hajnal property, the polynomial Rödl property, and virality are equivalent (For a finite family, the Erdős–Hajnal property, the polynomial Rödl property, and virality are equivalent).
Deleting a leaf from each of two forbidden graphs preserves virality (Deleting a leaf from each of two forbidden graphs preserves virality).
A graph is -free when it contains no induced copy of any member of (-free and -free graphs under the induced-subgraph convention).
Proof
By [L1], fix and so that the modified family has the Erdős-Hajnal property. By the implication from assertion 1 to assertion 3 in [L2], the family is viral.
Apply [L3] with both leaf-deletion slots equal to the same graph and with the same leaf . The two modified families are both , so step 1.1 makes them viral. Therefore itself is viral. Using the implication from assertion 3 to assertion 2 in [L2], choose such that every -free graph has an -restricted induced subgraph on at least times its number of vertices for every . Set .
Since is -free by [L4], step 2.1 applies to with . We obtain a -restricted induced subgraph of with at least vertices. Since , one has , so this induced subgraph also has at least vertices. Hence outcome 2 holds.
Because outcome 2 always holds, the displayed dichotomy is satisfied.
Iterated sparse restriction reaches the target sparsity threshold
Statement
Let , , , and assume Suppose that and that a graph satisfies:
- has a -sparse induced subgraph with at least vertices; and
- for every and every -sparse induced subgraph of with , there is a -sparse induced subgraph of with at least vertices.
Then contains an -sparse induced subgraph with at least vertices.
Facts & Assumptions
Given: The parameters and hypotheses in the statement.
A -sparse vertex set is nonempty, and every vertex has degree at most times the size of that set inside the induced subgraph (-sparse, -dense and -restricted vertex sets).
Proof
Hypothesis 1 supplies a nonempty -sparse vertex set, because -sparse sets are nonempty by [L1]. Hence .
For each nonempty induced subgraph of , let be its maximum degree divided by ; by [L1], the graph is -sparse exactly when . Define . Hypothesis 1 gives a -sparse induced subgraph with at least vertices, so and . Because has only finitely many induced subgraphs, the set of values with and has a minimum. Choose an induced subgraph for which that minimum is attained, and write .
Suppose that . Then hypothesis 2 applies to and produces a -sparse induced subgraph with at least vertices, where the last inequality uses . Since is -sparse, we have and therefore . Also . This contradicts the minimal choice of . Therefore .
Since , step 1.2 gives , and step 2.1 gives . Because is -sparse, it is also -sparse. Moreover .
The induced subgraph from step 3.1 is the required -sparse induced subgraph.
A sparse graph either sparsifies further or yields a comb or a large sparse pair
Statement
Let , and let be a -sparse graph with . Then at least one of the following holds:
- there are disjoint sets such that and is -sparse to ;
- is -sparse; or
- for some integer , there is an -comb in , and there is a vertex that is complete to and anticomplete to .
Facts & Assumptions
Given: Parameters and a graph satisfying the displayed hypotheses.
If outcomes 1 and 2 fail, then there exist a vertex and disjoint sets with , , , , -sparse to , and every vertex of having at least neighbours in (A sparse graph without a large sparse pair has a large nearly covered sparse pair).
If every vertex of a nonempty set has at least neighbours in a nonempty set , then some set with meets the neighbourhood in of at least half of the vertices of (A dense bipartite side has a small hitting set).
In a bipartite graph where every vertex of has a neighbour in and every vertex of has at most neighbours in , either there is a -comb for some integer , or (A bipartite graph with bounded A-degree has a large comb or a small B-side).
An -comb in a graph is a sequence of distinct teeth and pairwise disjoint blocks such that is complete to and anticomplete to every other block (Combs in a graph).
Proof
[assume-case pair] If outcome 1 already holds, there is nothing to prove.
[assume-case sparser] If outcome 2 already holds, there is nothing to prove.
[assume-case comb] Assume now that outcomes 1 and 2 both fail. Then [L1] gives a vertex and disjoint sets with , , and the displayed nearly covered sparse pair properties. In particular and are nonempty.
Apply [L2] with to the sets . We obtain a set with such that at least half of the vertices of have a neighbour in . Let be the set of vertices with a neighbour in ; then .
Consider the bipartite graph between and . Every vertex of has a neighbour in by definition. Since is -sparse to and , every vertex of has at most neighbours in . Apply [L3] with , , and .
The second alternative of [L3] is impossible for these parameters, because it would give . The constant in front of is less than , while since . Hence [L3] yields a -comb with each and each .
Because the teeth are distinct members of , we have . Also [L4] gives for each tooth block, while every has at most neighbours in . Since is complete to by [L4], this forces , and therefore . Finally from step 1.3, so each block has size at least .
By step 1.3, we already have a vertex with and . Since each and each , the vertex is complete to and anticomplete to . Together with step 5.1, this is exactly outcome 3.
The three cases 1.1, 1.2, and 1.3 exhaust the possibilities, so one of the stated outcomes always holds.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Shenwei Huang, Yiao Ju, and Yidong Zhou, Erdős-Hajnal beyond the five-vertex path, Section 2.2
- Tung Nguyen, Alex Scott, and Paul Seymour, Induced subgraph density. VII. The five-vertex path, Claim 5.2.1
- Shenwei Huang, Yiao Ju, and Yidong Zhou, Erdős-Hajnal beyond the five-vertex path, Lemma 2.10 context
- Shenwei Huang, Yiao Ju, and Yidong Zhou, Erdős-Hajnal beyond the five-vertex path, Lemma 2.7
- Tung Nguyen, Alex Scott, and Paul Seymour, Induced subgraph density. IV. New graphs with the Erdős-Hajnal property, Theorem 6.1
- Shenwei Huang, Yiao Ju, and Yidong Zhou, Erdős-Hajnal beyond the five-vertex path, Lemma 2.9
- Tung Nguyen, Alex Scott, and Paul Seymour, Induced subgraph density. VI. Bounded VC-dimension, Lemma 3.2
- Shenwei Huang, Yiao Ju, and Yidong Zhou, Erdős-Hajnal beyond the five-vertex path, Lemma 2.10
- Tung Nguyen, Alex Scott, and Paul Seymour, Induced subgraph density. VII. The five-vertex path, Lemma 5.2