Additive Approximation for Edge-Deletion Problems
Noga Alon ∗ Asaf Shapira † Benny Sudakov ‡
Abstract
A graph property is monotone if it is closed under removal of vertices and edges. In this
paper we consider the following algorithmic problem, called the edge-deletion problem; given a
monotone property P and a graph G, compute the smallest number of edge deletions that are
needed in order to turn G into a graph satisfying P. We denote this quantity by E ′ P
(G). The
first result of this paper states that the edge-deletion problem can be efficiently approximated for
any monotone property.
• For any fixed ɛ > 0 and any monotone property P, there is a deterministic algorithm, which
given a graph G = (V, E) of size n, approximates E ′ P (G) in linear time O(|V | + |E|) to
within an additive error of ɛn2 .
Given the above, a natural question is for which monotone properties one can obtain better
additive approximations of E ′ P . Our second main result essentially resolves this problem by giving
a precise characterization of the monotone graph properties for which such approximations exist.
(1) If there is a bipartite graph that does not satisfy P, then there is a δ > 0 for which it is
possible to approximate E ′ P to within an additive error of n2−δ in polynomial time.
(2) On the other hand, if all bipartite graphs satisfy P, then for any δ > 0 it is NP -hard to
approximate E ′ P to within an additive error of n2−δ .
While the proof of (1) is relatively simple, the proof of (2) requires several new ideas and involves
tools from Extremal Graph Theory together with spectral techniques. Interestingly, prior to this
precisely for the properties in (2) is NP -hard.
work it was not even known that computing E ′ P
We thus answer (in a strong form) a question of Yannakakis, who asked in 1981 if it is possible
to find a large and natural family of graph properties for which computing E ′ P is NP -hard.
∗ Schools of Mathematics and Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv
University, Tel Aviv 69978, Israel and IAS, Princeton, NJ 08540, USA. Email: nogaa@tau.ac.il. Research supported in
part by the Israel Science Foundation, by the Hermann Minkowski Minerva Center for Geometry at Tel Aviv University
and by the Von Neumann Fund.
† School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel
Aviv, Israel. Email: asafico@tau.ac.il. This work forms part of the author’s Ph.D. thesis. Research supported in part
by a Charles Clore Foundation Fellowship and an IBM Ph.D. Fellowship.
‡ Department of Mathematics, Princeton University, Princeton, NJ 08544, USA. E-mail: bsudakov@math.princeton.edu.
Research supported in part by NSF grants DMS-0355497, DMS-0106589, and by
an Alfred P. Sloan fellowship.
1
1 Introduction
1.1 Definitions, background and motivation
The topic of this paper is graph modification problems, namely problems of the type: ”given a graph
G, find the smallest number of modifications that are needed in order to turn G into a graph satisfying
property P”. The main two types of such problems are the following, in node modification problems,
one tries to find the smallest set of vertices, whose removal turns G into a graph satisfying P, while
in edge modification problems, one tries to find the smallest number of edge deletions/additions that
turn G into a graph satisfying P. In this paper we will focus on edge-modification problems. Before
continuing with the introduction we need to introduce some notations.
For a graph property P, let Pn denote the set of graphs on n vertices which satisfy P. Given two
graphs on n vertices, G and G ′ , we denote by ∆(G, G ′ ) the edit distance between G and G ′ , namely
the smallest number of edge additions and/or deletions that are needed in order to turn G into G ′ .
For a given property P, we want to denote how far is a graph G from satisfying P. For notational
reasons it will be more convenient to normalize this measure so that it is always in the interval [0, 1]
]). We thus define
(actually [0, 1
2
Definition 1.1 (EP(G)) For a graph property P and a graph G on n vertices, let
EP(G) = min
G ′ ∆(G, G
∈Pn
′ )
n2 .
In words, EP(G) is the minimum edit distance of G to a graph satisfying P after normalizing it by
a factor of n 2 .
Graph modification problems are well studied computational problems. In 1979, Garey and Johnson
[28] mentioned 18 types of vertex and edge modification problems. Graph modification problems
were extensively studied as these problems have applications in several fields, including Molecular
Biology and Numerical Algebra. In these applications a graph is used to model experimental data,
where edge modifications correspond to correcting errors in the data: Adding an edge means correcting
a false negative, while deleting an edge means correcting a false positive. Computing EP(G)
for appropriately defined properties P have important applications in physical mapping of DNA (see
[17], [29] and [31]). Computing EP(G) for other properties arises when optimizing the running time
of performing Gaussian elimination on a sparse symmetric positive-definite matrix (see [42]). Other
modification problems arise as subroutines for heuristic algorithms for computing the largest clique
in a graph (see [48]). Some edge modification problems also arise naturally in optimization of circuit
design [18]. We briefly mention that there are also many results about vertex modification problems,
notably that of Lewis and Yannakakis [38], who proved that for any nontrivial hereditary property
P, it is NP -hard to compute the smallest number of vertex deletions that turn a graph into one
satisfying P. (A graph property is hereditary if it is closed under removal of vertices.)
A graph property is said to be monotone if it is closed under removal of both vertices and edges.
Examples of well studied monotone properties are k-colorability, and the property of being H-free
for some fixed graph H. (A graph is H-free if it contains no copy of H as a not necessarily induced
subgraph.) Note, that when trying to turn a graph into one satisfying a monotone property we
will only use edge deletions. Therefore, in these cases the problem is sometimes called edge-deletion
problem. Our main results, presented in the following subsections, give a nearly complete answer to
the hardness of additive approximations of the edge-deletion problem for monotone properties.
2
1.2 An algorithm for any monotone property
Our first main result in this paper states that for any graph property P that belongs to the large,
natural and well studied family of monotone graph properties, it is possible to derive efficient approximations
of EP.
Theorem 1.1 For any fixed ɛ > 0 and any monotone property P there is a deterministic algorithm
that given a graph G on n vertices computes in time O(n 2 ) a real E satisfying |E − EP(G)| ≤ ɛ.
Note, that the running time of our algorithm is of type f(ɛ)n 2 , and can in fact be improved to linear
in the size of the input by first counting the number of edges, taking E = 0 in case the graph has
less than ɛn 2 edges. We note that Theorem 1.1 was not known for many monotone properties. In
particular, such an approximation algorithm was not even known for the property of being trianglefree
and more generally for the property of being H-free for any non-bipartite H.
Theorem 1.1 is obtained via a novel structural graph theoretic technique. One of the applications
of this technique (roughly) yields that every graph G, can be approximated by a small weighted graph
W , in such a way that EP(G) is approximately the optimal solution of a certain related problem (explained
precisely in Section 3) that we solve on W . The main usage of this new structural-technique
in this paper is in proving Lemmas 3.4 and 3.5 that lie at the core of the proof of Theorem 1.1.
This new technique, which may very well have other algorithmic and graph-theoretic applications,
applies a result of Alon, Fischer, Krivelevich and Szegedy [4] which is a strengthening of Szemerédi’s
Regularity Lemma [44]. We then use an efficient algorithmic version of the regularity lemma, which
also implies an efficient algorithmic version of the result of [4], in order to transform the existential
structural result into the algorithm stated in Theorem 1.1.
We further use our structural result in order to prove the following concentration-type result
regarding the edit distance of subgraphs of a graph.
Theorem 1.2 For every ɛ and any monotone property P there is a d = d(ɛ, P) with the following
property: Let G be any graph and suppose we randomly pick a subset D, of d vertices from V (G).
Denote by G ′ the graph induced by G on D. Then,
P rob[ |EP(G ′ ) − EP(G)| > ɛ] < ɛ .
An immediate implication of the above theorem is the following,
Corollary 1.2 For every ɛ > 0 and any monotone property P there is a randomized algorithm
that given a graph G computes in time O(1) a real E satisfying |E − EP(G)| ≤ ɛ with probability at
least 1 − ɛ.
We stress that there are some computational subtleties regrading the implementation of the
algorithmic results discussed above. Roughly speaking, one should define how the property P is
”given” to the algorithm and also whether ɛ is a fixed constant or part of the input. These issues
are discussed in Section 5.
It is natural to ask if the above results can be extended to the larger family of hereditary properties,
namely, properties closed under removal of vertices, but not necessarily under removal of edges.
Many natural properties such as being Perfect, Chordal and Interval are hereditary non-monotone
properties. By combining the ideas we used in order to prove Theorem 1.1 along with the main ideas
of [6] it can be shown that Theorem 1.1 (as well as Theorem 1.2 and Corollary 1.2) also hold for any
hereditary graph property.
3
1.3 On the possibility of better approximations
Theorem 1.1 implies that it is possible to efficiently approximate the distance of an n vertex graph
from any monotone graph property P, to within an error of ɛn 2 for any ɛ > 0. A natural question one
can ask is for which monotone properties it is possible to improve the additive error to n 2−δ for some
fixed δ > 0. In the terminology of Definition 1.1, this means to approximate EP to within an additive
error of n −δ for some δ > 0. Our second main result in this paper is a precise characterization of the
monotone graph properties for which such a δ > 0 exists 1 .
Theorem 1.3 Let P be a monotone graph property. Then,
1. If there is a bipartite graph that does not satisfy P, then there is a fixed δ > 0 for which it is
possible to approximate EP to within an additive error of n −δ in polynomial time.
2. On the other hand, if all bipartite graphs satisfy P, then for any fixed δ > 0 it is NP -hard to
approximate EP to within an additive error of n −δ .
While the first part of the above theorem follows easily from the known results about the Turán
numbers of bipartite graphs (see, e.g., [45]), the proof of the second item involves various combinatorial
tools. These include Szemerédi’s Regularity Lemma, and a new result in Extremal Graph Theory,
which is stated in Theorem 6.1 (see Section 6) that extends the main result of [14] and [15]. We
also use the basic approach of [1], which applies spectral techniques to obtain an NP -hardness result
by embedding a blow-up of a sparse instance to a problem, in an appropriate dense pseudo-random
graph. Theorem 6.1 and the proof technique of Theorem 1.3 may be useful for other applications
in graph theory and in proving hardness results. As in the case of Theorem 1.1, the second part of
Theorem 1.3 was not known for many specific monotone properties. For example, prior to this paper
it was not even known that it is NP -hard to precisely compute EP, where P is the property of being
triangle-free. More generally, such a result was not known for the property of being H-free for any
non-bipartite H.
1.4 Related work
Our main results form a natural continuation and extension of several research paths that have been
extensively studied. Below we survey some of them.
1.4.1 Approximations of graph-modification problems
As we have previously mentioned many practical optimization problems in various research areas
can be posed as the problem of computing the edit-distance of a certain graph from satisfying a
certain property. Cai [16] has shown that for any hereditary property, which is expressible by a
finite number of forbidden induced subgraphs, the problem of computing the edit distance is fixedparameter
tractable. Khot and Raman [33] proved that for some hereditary properties P, finding in
a given graph G, a subgraph that satisfies P is fixed-parameter tractable, while for other properties
finding such a subgraph is hard in an appropriate sense (see [33]).
Note that Theorem 1.1 implies that if the edit distance (in our case, number of edge removals) of
a graph from a property is Ω(n 2 ), then it can be approximated to within any multiplicative constant
1 + ɛ.
1 We assume henceforth that P is not satisfied by all graphs.
4
1.4.2 Hardness of edge-modification problems
Natanzon, Shamir and Sharan [39] proved that for various hereditary properties, such as being
Perfect and Comparability, computing EP is NP -hard and sometimes even NP -hard to approximate
to within some constant. Yannakakis [46] has shown that for several graph properties such as
outerplanar, transitively orientable, and line-invertible, computing EP is NP -hard. Asano [12] and
Asano and Hirata [13] have shown that properties expressible in terms of certain families of forbidden
minors or topological minors are NP -hard.
The NP -completeness proofs obtained by Yannakakis in [46], were add-hoc arguments that applied
only to specific properties. Yannakakis posed in [46] as an open problem, the possibility of
proving a general NP -hardness result for computing EP that will apply to a general family of graph
properties. Theorem 1.3 achieves such a result even for the seemingly easier problem of approximating
EP.
1.4.3 Approximation schemes for ”dense” instances
Fernandez de la Vega [22] and Arora, Karger and Karpinski [11] showed that many of the classical
NP -complete problems such as MAX-CUT and MAX-3-CNF have a PTAS when the instance
is dense, namely if the graph has Ω(n 2 ) edges or the 3-CNF formula has Ω(n 3 ) clauses. Approximations
for dense instances of Quadratic Assignment Problems, as well as for additional problems,
were obtained by Arora, Frieze and Kaplan [10]. Frieze and Kannan [26] obtained approximations
schemes for several dense graph theoretic problems via certain matrix approximations. Alon, Fernandez
de la Vega, Kannan and Karpinski [3] obtained results analogous to ours for any dense
Constraint-Satisfaction-Problem via certain sampling techniques. It should be noted that all the
above approximation schemes are obtained in a way similar to ours, that is, by first proving an additive
approximation, and then arguing that in case the optimal solution is large (that is, Ω(n 2 ) in case
of graphs, or Ω(n 3 ) in case of 3-CNF) the small additive error translates into a small multiplicative
error.
All the above approximation results apply to the family of so called Constraint-Satisfaction-
Problems. In some sense, these problems can express graph properties for which one imposes restrictions
on pairs of vertices, such as k-colorability. These techniques thus fall short from applying
to properties as simple as Triangle-freeness, where the restriction is on triples of vertices. The techniques
we develop in order to obtain Theorem 1.1 enable us to handle restrictions that apply to
arbitrarily large sets of vertices.
We briefly mention that EP is related to packing problems of graphs. In [32] and [47] it was
shown that by using linear programming one can approximate the packing number of a graph. In
Section 9 we explain why this technique does not allow one to approximate EP.
1.4.4 Algorithmic applications of Szemerédi’s Regularity Lemma
The authors of [2] gave a polynomial time algorithmic version of Szemerédi’s Regularity Lemma.
They used it to prove that Theorem 1.1 holds for the k-colorability property. The running time of
their algorithm was improved by Kohayakawa, Rödl and Thoma [34]. Frieze and Kannan [25] further
used the algorithmic version of the regularity lemma, to obtain approximation schemes for additional
graph problems.
Theorem 1.1 is obtained via the algorithmic version of a strengthening of the standard regularity
lemma, which was proved in [4], and it seems that these results cannot be obtained using the standard
regularity lemma.
5
1.4.5 Tolerant Property-Testing
In standard Property-Testing (see [23] and [41]) one wants to distinguish between the graphs G that
satisfy a certain graph property P, or equivalently those G for which EP(G) = 0, from those that
satisfy EP(G) > ɛ. The main goal in designing property-testers is to reduce their query-complexity,
namely, minimize the number of queries of the form ”are i and j connected in the input graphs?”.
Parnas, Ron and Rubinfeld [40] introduced the notion of Tolerant Property-Testing, where one
wants to distinguish between the graphs G that satisfy EP(G) < δ from those that satisfy EP(G) > ɛ,
where 0 ≤ δ < ɛ ≤ 1 are some constants. Recently, there have been several results in this line of
work. Specifically, Fischer and Newman [24] have recently shown that if a graph property is testable
with number of queries depending on ɛ only, then it is also tolerantly testable for any 0 ≤ δ < ɛ ≤ 1
and with query complexity depending on |ɛ − δ|. Combining this with the main result of [7] implies
that any monotone property is tolerantly testable for any 0 ≤ δ < ɛ ≤ 1 and with query complexity
depending on |ɛ − δ|. Note, that Corollary 1.2 implicitly states the same. In fact, the algorithm
implied by Corollary 1.2 is the ”natural” one, where one picks a random subset of vertices S, and
approximates EP(G) by computing EP on the graph induced by S. The algorithm of [24] is far more
complicated. Furthermore, due to the nature of our algorithm if the input graph satisfies a monotone
property P, namely if EP(G) = 0, we will always detect that this is the case. The algorithm of [24]
may declare that EP(G) > 0 even if EP(G) = 0.
1.5 Organization
The proofs of the main results of this paper, Theorems 1.1 and 1.3, are independent of each other.
Sections 2, 3, 4 and 5 contain the proofs relevant to Theorem 1.1 and Sections 6, 7 and 8 contain
the proofs relevant to Theorem 1.3.
In Section 2 we introduce the basic notions of regularity and state the regularity lemmas that we
use for proving Theorem 1.1 and some of their standard consequences. In Section 3 we give a high
level description of the main ideas behind our algorithms. We also state the main structural graph
theoretic lemmas, Lemmas 3.4 and 3.5 that lie at the core of these algorithms. The proofs of these
lemmas appear in section 4. In Section 5 we give the proof of Theorems 1.1 and 1.2 as well as a
discussion about some subtleties regarding the implementation of these algorithms.
Section 6 contains a high-level description of the proof of Theorem 1.3 as well as a description of
the main tools that we apply in this proof. In Section 7 we prove a new Extremal Graph-Theoretic
result that lies at the core of the proof of Theorem 1.3. In Section 8 we give the detailed proof of
Theorem 1.3.
The final Section 9 contains some concluding remarks and open problems. Throughout the
paper, whenever we relate, for example, to a function f3.1, we mean the function f defined in
Lemma/Claim/Theorem 3.1.
2 Regularity Lemmas and their Algorithmic Versions
In this section we discuss the basic notions of regularity, some of the basic applications of regular
partitions and state the regularity lemmas that we use in the proof of Theorems 1.1 and 1.2. See
[35] for a comprehensive survey on the regularity-lemma. We start with some basic definitions. For
every two nonempty disjoint vertex sets A and B of a graph G, we define e(A, B) to be the number
of edges of G between A and B. The edge density of the pair is defined by d(A, B) = e(A, B)/|A||B|.
6
Definition 2.1 (γ-regular pair) A pair (A, B) is γ-regular, if for any two subsets A ′ ⊆ A and
B ′ ⊆ B, satisfying |A ′ | ≥ γ|A| and |B ′ | ≥ γ|B|, the inequality |d(A ′ , B ′ ) − d(A, B)| ≤ γ holds.
Throughout the paper we will make an extensive use of the notion of graph homomorphism which
we turn to formally define.
Definition 2.2 (Homomorphism) A homomorphism from a graph F to a graph K, is a mapping
ϕ : V (F ) ↦→ V (K) that maps edges to edges, namely (v, u) ∈ E(F ) implies (ϕ(v), ϕ(u)) ∈ E(K).
In what follows, F ↦→ K denotes the fact that there is a homomorphism from F to K. We will
also say that a graph H is homomorphic to K if H ↦→ K. Note, that a graph H is homomorphic to
a complete graph of size k if and only if H is k-colorable.
Let F be a graph on f vertices and K a graph on k vertices, and suppose F ↦→ K. Let G be a
graph obtained by taking a copy of K, replacing every vertex with a sufficiently large independent
set, and every edge with a random bipartite graph of edge density d. It is easy to show that with
high probability, G contains a copy of F (in fact, many). The following lemma shows that in order
to infer that G contains a copy of F , it is enough to replace every edge with a ”regular enough” pair.
Intuitively, the larger f and k are, and the sparser the regular pairs are, the more regular we need
each pair to be, because we need the graph to be ”closer” to a random graph. This is formulated
in the lemma below. Several versions of this lemma were previously proved in papers using the
regularity lemma (see [35]).
Lemma 2.3 For every real 0 < η < 1, and integers k, f ≥ 1 there exist γ = γ2.3(η, k, f), and
N = N2.3(η, k, f) with the following property. Let F be any graph on f vertices, and let U1, . . . , Uk
be k pairwise disjoint sets of vertices in a graph G, where |U1| = . . . = |Uk| ≥ N. Suppose there
is a mapping ϕ : V (F ) ↦→ {1, . . . , k} such that the following holds: If (i, j) is an edge of F then
(U ϕ(i), U ϕ(j)) is γ-regular with density at least η. Then U1, . . . , Uk span a copy of F .
Comment 2.4 Observe that the function γ2.3(η, k, f) may and will be assumed to be monotone
non-increasing in k and f and monotone non-decreasing in η. Therefore, it will be convenient
to assume that γ2.3(η, k, f) ≤ η 2 . Similarly, we will assume that N2.3(η, k, f) is monotone nondecreasing
in k and f. Also, for ease of future definitions (in particular those given in (2)) set
γ2.3(η, k, 0) = N2.3(η, k, 0) = 1 for any k ≥ 1 and 0 < η < 1.
A partition A = {Vi | 1 ≤ i ≤ k} of the vertex set of a graph is called an equipartition if |Vi| and
|Vj| differ by no more than 1 for all 1 ≤ i < j ≤ k (so in particular each Vi has one of two possible
sizes). The order of an equipartition denotes the number of partition classes (k above). A refinement
of an equipartition A is an equipartition of the form B = {Vi,j | 1 ≤ i ≤ k, 1 ≤ j ≤ l} such that Vi,j
is a subset of Vi for every 1 ≤ i ≤ k and 1 ≤ j ≤ l.
Definition 2.5 (γ-regular equipartition) An equipartition B = {Vi | 1 ≤ i ≤ k} of the vertex set
of a graph is called γ-regular if all but at most γ � � k
2 of the pairs (Vi, Vi ′) are γ-regular.
The Regularity Lemma of Szemerédi can be formulated as follows.
Lemma 2.6 ([44]) For every m and γ > 0 there exists T = T2.6(m, γ) with the following property:
If G is a graph with n ≥ T vertices, and A is an equipartition of the vertex set of G of order at most
m, then there exists a refinement B of A of order k, where m ≤ k ≤ T and B is γ-regular.
7
T2.6(m, γ) may and is assumed to be monotone non-decreasing in m and monotone non-increasing
in γ. Szemerédi’s original proof of Lemma 2.6 was only existential as it supplied no efficient algorithm
for obtaining the required equipartition. Alon et. al. [2] were the first to obtain a polynomial time
algorithm for finding the equipartition, whose existence is guaranteed by lemma 2.6. The running
time of this algorithm was improved by Kohayakawa et. al. [34] who obtained the following result.
Lemma 2.7 ([34]) For every fixed m and γ there is an O(n 2 ) time algorithm that given an equipartition
A finds equipartition B as in Lemma 2.6.
Our main tool in the proof of Theorem 1.1 is Lemma 2.9 below, proved in [4]. This lemma can
be considered a strengthening of Lemma 2.6, as it guarantees the existence of an equipartition and
a refinement of this equipartition that poses stronger properties compared to those of the standard
γ-regular equipartition. This stronger notion is defined below.
Definition 2.8 (E-regular equipartition) For a function E(r) : N ↦→ (0, 1), a pair of equipartitions
A = {Vi | 1 ≤ i ≤ k} and its refinement B = {Vi,j | 1 ≤ i ≤ k, 1 ≤ j ≤ l}, where Vi,j ⊂ Vi for
all i, j, are said to be E-regular if
1. For all 1 ≤ i < i ′ ≤ k, for all 1 ≤ j, j ′ ≤ l but at most E(k)l2 of them, the pair (Vi,j, Vi ′ ,j ′) is
E(k)-regular.
2. All 1 ≤ i < i ′ ≤ k but at most E(0) �k 2
E(0)l2 of them |d(Vi, Vi ′) − d(Vi,j, Vi ′ ,j
� of them are such that for all 1 ≤ j, j ′ ≤ l but at most
′)| < E(0) holds.
It will be very important for what follows to observe that in Definition 2.8 we may use an arbitrary
function rather than a fixed γ as in Definition 2.5 (such functions will be denoted by E throughout
the paper). The following is one of the main results of [4].
Lemma 2.9 ([4]) For any integer m and function E(r) : N ↦→ (0, 1) there is S = S2.9(m, E) such
that any graph on at least S vertices has an E-regular equipartition A, B where |A| = k ≥ m and
|B| = kl ≤ S.
In order to make the presentation self contained we briefly review the proof of Lemma 2.9. Fix
any m and function E and put ζ = E(0). Partition G into m arbitrary subsets of equal size and
denote this equipartition by A0. Put M = m. Iterate the following task: Apply Lemma 2.6 on Ai−1
with m = |Ai−1| and γ = E(M)/M 2 and let Ai be the refinement of Ai−1 returned by Lemma 2.6.
If Ai−1 and Ai form an E-regular equipartition stop, otherwise set M = |Ai−1| and reiterate. It is
shown is [4] that after at most 100/ζ 4 iterations, for some 1 ≤ i ≤ 100/ζ 4 the partitions Ai−1 and Ai
form an E-regular equipartition. Moreover, detecting an i for which this holds is very easy, that is,
can be done in time O(n 2 ) (see the proof in [4]). Note, that one can thus set the integer S2.9(m, E) to
be the order of Ai. In particular, the following is an immediate implication of the above discussion.
Proposition 2.10 If m is bounded by a function of ɛ only, then for any E the integer S = S2.9(m, E)
can be upper bounded by a function of ɛ only.
The ɛ in the above proposition will be the ɛ from the task of approximating EP within an error
of ɛ in Theorem 1.1. Also, in our application of Lemma 2.9 the function E will (implicitly) depend
on ɛ. For example, it will be convenient to set E(0) = ɛ. However, it follows from the definition of
8
S2.9(m, E) given above that even in this case it is possible to upper bound S2.9(m, E) by a function
of ɛ only.
In order to design our algorithm we will need to obtain the equipartitions A and B that appear
in the statement of Lemma 2.9. However, note that by the overview of the proof of Lemma 2.9 given
above, in order to obtain this partition one can use Lemma 2.7 as an efficient algorithm for obtaining
the regular partitions. Moreover, by Proposition 2.10 whenever we apply either E or Lemma 2.7 we
are guaranteed that m (which in the above overview was M) is upper bounded by some function
of ɛ and γ is lower bounded by some function of ɛ. This means that each of the at most 100/ζ 4
applications of Lemma 2.10 takes O(n 2 ) time. We thus get the following:
Proposition 2.11 If m is bounded by a function of ɛ only, then for any E there is an O(n 2 ) algorithm
for obtaining the equipartitions A and B of Lemma 2.9.
3 Overview of the Proof of Theorem 1.1
We start with a convenient way of handling a monotone graph property.
Definition 3.1 (Forbidden Subgraphs) For a monotone graph property P, define F = FP to be
the set of graphs which are minimal with respect to not satisfying property P. In other words, a graph
F belongs to F if it does not satisfy P, but any graph obtained from F by removing an edge or a
vertex, satisfies P.
As an example of a family of forbidden subgraphs, consider P which is the property of being 2colorable.
Then FP is the set of all odd-cycles. Clearly, a graph satisfies P if and only it contains no
member of FP as a (not necessarily induced) subgraph. We say that a graph is F-free if it contains
no (not necessarily induced) subgraph F ∈ F. Clearly, for any family F, being F-free is a monotone
property. Thus, the monotone properties are precisely the graph properties that are equivalent to
being F-free for some family F. In order to simplify the notation, it will be simpler to talk about
properties of type F-free rather than monotone properties. To avoid confusion we will henceforth
denote by EF(G) the value of EP(G), where F = FP as above.
The main idea we apply in order to obtain the algorithmic results of this paper is quite simple;
given a graph G, a family of forbidden subgraphs F and ɛ > 0 we use Lemma 2.9 with appropriately
defined parameters in order to construct in O(n 2 ) time a weighted complete graph W , of size depending
on ɛ but independent of the size of G, such that a solution of a certain ”related” problem
on W gives a good approximation of EF(G). As W will be of size independent of the size of G, we
may and will use exhaustive search in order to solve the ”related” problem on W . In what follows
we give further details on how to define W and the ”related” problem that we solve on W .
We start with the simplest case, where the property is that of being triangle-free, namely F =
{K3}. Let W be some weighted complete graph on k vertices and let 0 ≤ w(i, j) ≤ 1 denote the
weight of the edge connecting i and j in W . Let EF(W ) be the natural extension of the definition
of EF(G) to weighted graphs, namely, instead of just counting how many edges should be removed
in order to turn G into an F-free graph, we ask for the edge set of minimum weight with the above
property. Let G be a k-partite graph on n vertices with partition classes V1, . . . , Vk of equal size n/k.
Suppose for every i < j we have d(Vi, Vj) = w(i, j) (recall that d(Vi, Vj) denotes the edge density
between Vi and Vj). In some sense, W can be considered a weighted approximation of G, but to our
investigation a more important question is whether W can be used in order to estimate EF(G)? In
other words, is it true that EF(G) ≈ EF(W )?
9
It is easy to see that EF(G) ≤ EF(W ). Indeed, given a set of edges S, whose removal turns W
into a triangle free graph, we simply remove all edges connecting Vi and Vj for every (i, j) ∈ S. The
main question is whether the other direction is also true. Namely, is it true that if it is possible to
remove αn 2 from G and thus make it triangle free, then it is possible to remove from W a set of
edges of total weight approximately αk 2 and thus make it triangle-free? If true this will mean that
by computing EF(W ) we also approximately compute EF(G). Unfortunately, this assertion is false
in general, as the minimal number of edge modifications that are enough to make G triangle-free,
may involve removing some and not all the edges connecting a pair (Vi, Vj), and in W we can remove
only edges and not parts of them. It thus seems natural to ask what kind of restrictions should we
impose on G (or more precisely on the pairs (Vi, Vj)) such that the above situation will be impossible,
namely, that the optimal way to turn G into a triangle free graph will involve removing either none
or all the edges connecting a pair (Vi, Vj) (up to some small error). This will clearly imply that we
also have EF(G) ≈ EF(W ).
One natural restriction is that the pairs (Vi, Vj) would be random bipartite graphs. While this
restriction indeed works it is of no use for our investigation as we are trying to design an algorithm
that can handle arbitrary graphs and not necessarily random graphs. One is thus tempted to replace
random bipartite graph with γ-regular pairs for some small enough γ. Unfortunately, we did not
manage to prove that there is a small enough γ > 0 ensuring that even if all pairs (Vi, Vj) are
γ-regular then EF(G) ≈ EF(W ). In order to circumvent this difficulty we use the stronger notion
of E-regularity defined in Section 2. As it turns out, if one uses an appropriately defined function
E, then if all pairs (Vi, Vj) are E(k)-regular, one can infer that EF(G) ≈ EF(W ). This result is
(essentially) formulated in Lemma 3.4.
In the above discussion we considered the case F = {K3}. So suppose now that F is an arbitrary
(possibly infinite) family of graph. Suppose we use a weighted complete graph W on k vertices as
above in order to approximate some k-partite graph. The question that naturally arises at this stage
is what problem should we try to solve on W in order to get an approximation of EF(G). It is easy
to see that G may be very far from being F-free, while at the same time W can be F-free, simply
because F does not contain graphs of size at most k. As an example, consider the case, where the
property is that of containing no copy of the complete bipartite graph with two vertices in each
side, denoted K2,2. Now, if G is the complete bipartite graph K n/2,n/2 then it is very far from being
K2,2-free. However, in this case W is just an edge that spans no copy of K2,2.
It thus seems that we must solve a different problem on W . To formulate this problem we need
the following definitions.
Definition 3.2 (F-homomorphism-free) For a family of graphs F, a graph W is called Fhomomorphism-free
if F �↦→
W for any F ∈ F.
We now define a measure analogous to EF but with respect to making a graph F-homomorphismfree.
Note that we focus on weighted graphs.
Definition 3.3 (HF(W )) For a family of graphs F and a weighted complete graph W on k vertices,
let H ′ F(W ) denote the minimum total weight of a set of edges, whose removal from W turns it into
an F-homomorphism-free graph. Define, HF(W ) = H ′ F(W )/k 2 .
Note, that in Definition 3.2 the graph W is an unweighed not necessarily complete graph. Also,
observe that when F = {K3} then we have HF(W ) = EF(W ). As it turns out, the ”right” problem
to solve on W is to compute HF(W ). This is formulated in the following key lemma, whose proof
appears in Section 4:
10
Lemma 3.4 (The Key Lemma) For every family of graphs F, there are functions N3.4(k, ɛ) and
γ3.4(k, ɛ) with the following property 2 : Let W be any weighted complete graph on k vertices and let
G be any k-partite graph with partition classes V1, . . . , Vk of equal size such that
1. |V1| = . . . = |Vk| ≥ N3.4(k, ɛ).
2. All pairs (Vi, Vj) are γ3.4(k, ɛ)-regular.
3. For every 1 ≤ i < j ≤ k we have d(Vi, Vj) = w(i, j).
Then, EF(G) ≥ HF(W ) − ɛ .
It is easy to argue as we did above and prove that EF(G) ≤ HF(W ) in Lemma 3.4 (see the proof
of Lemma 3.5), however we will not need this (trivial) direction. It is important to note that while
Lemma 3.4 is very strong as it allows us to approximate EF(G) via computing HF(W ) (recall that
W is intended to be very small compared to G) its main weakness is that it requires the regularity
between each of the pairs to be a function of k, which denotes the number of partition classes, rather
than depending solely on the family of graphs F. We note that even if F = {K3} as discussed above,
we can only prove Lemma 3.4 with a regularity measure that depends on k. This supplies some
explanation as to why Lemma 2.6 (the standard regularity lemma) is not sufficient for our purposes;
note that the input to Lemma 2.6 is some fixed γ > 0 and the output is a γ-regular equipartition
with number of partition classes that depends on γ (the function T2.6(m, γ)). Thus, even if all pairs
are γ-regular, this γ may be very large when considering the number of partition classes returned by
Lemma 2.6 and the regularity measure which Lemma 3.4 requires. Hence, the standard regularity
lemma cannot help us with applying Lemma 3.4. In order to overcome this problem we use the
notion of E-regular partitions and the stronger regularity-lemma given in Lemma 2.9, which, when
appropriately used, allows us to apply Lemma 3.4 in order to obtain Lemma 3.5 below, from which
Theorem 1.1 follows quite easily. The proof of this lemma appears in Section 4.
Lemma 3.5 For any ɛ > 0 and family of graphs F there are functions N3.5(r) and E3.5(r) satisfying
the following 3 : Suppose a graph G has an E3.5-regular equipartition A = {Vi | 1 ≤ i ≤ k}, B =
{Vi,j | 1 ≤ i ≤ k, 1 ≤ j ≤ l}, where
1. k ≥ 1/ɛ.
2. |Vi,j| ≥ N3.5(k) for every 1 ≤ i ≤ k and 1 ≤ j ≤ l.
Let W be a weighted complete graph on k vertices with w(i, j) = d(Vi, Vj). Then,
|EF(G) − HF(W )| ≤ ɛ .
Using the algorithmic version of Lemma 2.9, which is given in Proposition 2.11, we can rephrase
the above lemma in a more algorithmic way, which is more or less the algorithm of Theorem 1.1: Given
a graph G we use the O(n 2 ) time algorithm of Proposition 2.11 in order to obtain the equipartition
described in the statement of Lemma 3.5. We then construct the graph W as in Lemma 3.5, and
finally use exhaustive search in order to precisely compute HF(W ). By Lemma 3.5, this gives a good
approximation of EF(G). The proof of Theorem 1.1 appears in Section 5.
2 The functions N3.4(k, ɛ) and γ3.4(k, ɛ) will also (implicitly) depend on F.
3 The functions N3.5(r) and E3.5(r) will also (implicitly) depend on ɛ and F.
11
4 Proofs of Lemmas 3.4 and 3.5
In this section we apply our new structural technique in order to prove Lemmas 3.4 and 3.5. Regretfully,
it is hard to precisely state what are the ingredients of this technique. Roughly speaking,
it uses the notion of E-regularity in order to partition the edges of a graph into a bounded number
of edge sets, which have regular-partitions that are almost identical 4 and more importantly, the
regularity-measure of each of the bipartite graphs in each of the edge sets can be a function of the
number of clusters.
We start this section with some definitions that will be very useful for the proof of Lemma 3.4.
Definition 4.1 For any (possibly infinite) family of graphs F, and any integer r let Fr be the
following set of graphs: A graph R belongs to Fr if it has at most r vertices and there is at least one
F ∈ F such that F ↦→ R.
Definition 4.2 For any family of graphs F and integer r for which Fr �= ∅, define
ΨF(r) = max
R∈Fr
min |V (F )|. (1)
{F ∈F:F ↦→R}
Define ΨF(r) = 0 if Fr = ∅. Therefore, ΨF(r) is monotone non-decreasing in r.
Practicing definitions, note that if F is the family of odd cycles, then Fk is precisely the family of
non-bipartite graphs of size at most k. Also, in this case ΨF(k) = k when k is odd, and ΨF(k) = k−1
when k is even. The ”right” way to think of the function ΨF is the following: Let R be a graph of
size at most k and suppose we are guaranteed that there is a graph F ′ ∈ F such that F ′ ↦→ R (thus
R ∈ Fk). Then by this information only and without having to know the structure of R itself, the
definition of ΨF implies that there is a graph F ∈ F of size at most ΨF(k), such that F ↦→ R.
The function ΨF has a critical role in the proof of Lemma 3.4. While proving this lemma we will
use Lemma 2.3 in order to derive that some k sets of vertices, which are regular enough, span some
graph F ∈ F. Roughly speaking, the main difficulty will be that we will not know the size of F ,
and as a consequence will not know the regularity measure between these sets that is sufficient for
applying Lemma 2.3 on these k sets (this quantity is γ2.3(η, k, |V (F )|)). However, we will know that
there is some F ′ ∈ F which is spanned by these sets. The function ΨF(r) will thus be very useful
as it supplies an upper bound for the size of the smallest F ∈ F which is spanned by these sets. See
Proposition 4.4, where ΨF(r) has a crucial role.
Proof of Lemma 3.4: Given ɛ and k let
T = T (k, ɛ) = T2.6(k, γ2.3(ɛ/2, k, ΨF(k))). (2)
We prove the lemma with γ3.4(k, ɛ) and N3.4(k, ɛ) satisfying
γ3.4(k, ɛ) = min(ɛ/2, 1/T ), (3)
N3.4(k, ɛ) = T · N2.3(ɛ/2, k, ΨF(k)) (4)
Suppose G is a graph on n vertices, in which case each set Vi is of size n.
We may thus show
k
that one must remove at least HF(W ) · n2 − ɛn2 edges from G in order to make it F-free. To this
4 Two regular partitions V1, . . . , Vk and U1, . . . , Uk are identical if d(Vi, Vj) = d(Ui, Uj)
12
end, it is enough to show that if there is a graph G ′ that is obtained from G by removing less than
HF(W ) · n2 − ɛn2 edges and spans no F ∈ F then it is possible to remove from W a set of edges of
total weight less than HF(W ) · k2 and obtain a graph W ′ that is F-homomorphism-free. This will
obviously be a contradiction.
Assume such a G ′ exists and apply Lemma 2.6 on it with γ = γ2.3( 1
2ɛ, k, ΨF(k)) and m = k (we
use m = k as G is already partitioned into k subsets V1, . . . , Vk). For the rest of the proof we denote
by Vi,1, . . . , Vi,l the partition of Vi that Lemma 2.6 returns. Recall that as |V1| = . . . = |Vk| and
Lemma 2.6 partitions a graph into subsets of equal size, then all the sets Vi are partitioned into the
same number l of subsets. Note also that by Lemma 2.6 and the definition of T in (2) we have l < T .
Observe, that T is in fact an upper bound for the total number of partition classes Vi,j).
By Lemma 2.6 (recall that by Comment 2.4 we may assume γ2.3( 1
2ɛ, k, ΨF(k)) ≤ 1
2ɛ), we are
guaranteed that out of the lk sets Vi,j at most ɛ
� � lk
2 2 pairs are not γ2.3( 1
2ɛ, k, ΨF(k))-regular. We
define a graph G ′′ , which is obtained from G ′ by removing all the edges connecting pairs (Vi,i ′, Vj,j ′)
that are not γ2.3( 1
′, Vj,j ′) for which their edge
2ɛ, k, ΨF(k))-regular, and all edges connecting pairs (Vi,i
density in G ′ is smaller than 1
2ɛ. Proposition 4.3 There are k sets V1,t1 , . . . , Vk,tk such that the graphs induced by G and G′′ on these
k sets differ by less than HF(W ) · n2
l 2 − ɛn2
2l 2 edges.
Proof: We first claim that G ′′ is obtained from G ′ by removing less than ɛ
2 n2 edges. To see this
note that the number of edges connecting a pair (Vi,i ′, Vj,j ′) is at most (n/kl)2 . As there are at most
ɛ
2
� lk
2
� pairs that are not γ2.3( 1
2 ɛ, k, ΨF(k))-regular, we remove at most ɛ
Finally, as due to pairs, whose edge density is at most 1
2ɛ, we remove at most �kl 4n2 edges due to such pairs.
�
ɛ
2 2 (n/kl)2 ≤ ɛ
4n2 edges, the total number of edges removed is at most ɛ
2 n2 , as needed.
As we assume that G ′ is obtained from G by removing less than HF(W ) · n 2 − ɛn 2 edges, we get
from the previous paragraph that G ′′ is obtained from G be removing less than HF(W ) · n 2 − ɛ
2 n2
edges. Suppose for every 1 ≤ i ≤ k we randomly and uniformly pick one of the sets Vi,1, . . . , Vi,l.
The probability that an edge, which belongs to G and not to G ′′ , is spanned by these k sets is l −2 .
As G and G ′′ differ by less than HF(W ) · n 2 − ɛ
2 n2 edges, we get that the expected number of such
edges is less than HF(W ) · n2
l2 − ɛn2
2l2 and therefore there must be a choice of k sets that span less than
this number of such edges.
We are now ready to arrive at a contradiction by showing that if it is possible to remove less than
HF(W ) · n 2 − ɛn 2 edges from G and thus turn it into an F-free graph G ′ , then we can remove from
W a set of edges of total weight less than HF(W ) · k2 and thus turn it into an F-homomorphism-free
graph W ′ . Let V1,i1 , . . . , Vk,ik be the k sets satisfying the condition of Proposition 4.3 and obtain
from W a graph W ′ by removing from W edge (i, j) if and only if the density of (Vi,ti , Vj,tj ) in G′′
is 0.
Proposition 4.4 W ′ is F-homomorphism-free.
Proof: Assume F ′ ↦→ W ′ for some F ′ ∈ F. As W ′ is a graph of size k this means (recall Definition
4.2) that there is F ∈ F of size at most ΨF(k) such that F ↦→ W ′ . Let ϕ be a homomorphism from F
to W ′ . By definition of ϕ, for any (u, v) ∈ E(F ) we have (ϕ(u), ϕ(v)) is an edge of W ′ . Recall that by
definition of G ′′ either the density of a pair (Vi,i ′, Vj,j ′) in G′′ is zero, or this density is at least 1
2ɛ and
the pair is γ2.3( 1
2ɛ, k, ΨF(k))-regular. By definition of W ′ , this means that for every (u, v) ∈ E(F )
the pair (Vϕ(u),tϕ(u) , Vϕ(v),tϕ(v) ) has density at least ɛ
2 in G′′ and is γ2.3( 1
2ɛ, k, ΨF(k))-regular. By item
13
1 of the lemma we have for all 1 ≤ i ≤ k that |Vi| ≥ N3.4(k, ɛ). By our choice in (4) and the fact
that l ≤ T , the sets Vi,ti must therefore be of size at least
|N3.4(k, ɛ)|/l ≥ |N3.4(k, ɛ)|/T = N2.3( 1
ɛ, k, ΨF(k)).
2
Hence, the sets V1,t1 , . . . , Vk,tk satisfy all the necessary requirements needed in order to apply Lemma
2.3 on them in order to deduce that they span a copy of F in G ′′ (recall, that we have already argued
that |V (F )| ≤ ΨF(k)). This, however, is impossible, as we assumed that G ′ was already F-free and
G ′′ is a subgraph of G ′ .
Proposition 4.5 For any i < j the edge densities of (Vi, Vj) and (Vi,ti , Vj,tj ) satisfy in G
1
|d(Vi, Vj) − d(Vi,ti , Vj,tj )| ≤
2 ɛ.
Proof: Recall that 1/l > 1/T and by (3) we have 1/T > γ3.4(k, ɛ). We infer that |Vi,ti | = |Vi|/l ≥
γ3.4(k, ɛ)|Vi|. By item 2 of the lemma, each pair (Vi, Vj) is γ3.4(k, ɛ)-regular in G. Hence, by definition
of a regular pair, we must have |d(Vi, Vj) − d(Vi,ti , Vj,tj )| ≤ γ3.4(k, ɛ) ≤ 1
2 ɛ.
Proposition 4.6 W ′ is obtained from W by removing a set of edges of weight less than HF(W ) · k 2 .
Proof: Let S be the set of edges removed from W and denote by w(S) the total weight of edges in
S. Let e(Vi,ti , Vj,tj ) denote the number of edges connecting the pair (Vi,ti , Vj,tj ) in G. We claim that
the following series of inequalities, which imply that w(S) < HF(W ) · k2 , hold:
HF(W ) · n2 ɛn2
�
− >
l2 2l2 (i,j)∈S
≥ �
(i,j)∈S
≥ �
(i,j)∈S
e(Vi,ti , Vj,tj )
(w(i, j) − ɛ
2
) n2
l 2 k 2
w(i, j) n2
l2 ɛn2
−
k2 2l2 = w(S) n2
l2 ɛn2
− .
k2 2l2 Indeed, recall that by the definition of W ′ , we have (i, j) ∈ S if and only if the density of the
pair (Vi,i ′, Vj,j ′) in G′′ is 0, which means that all the edges connecting this pair were removed in G ′′ .
As by Proposition 4.3 the total difference between G and G ′′ is less than HF(W ) · n2
l2 − ɛn2
2l2 we infer
that the first (strict) inequality is valid. The second inequality follows from Proposition 4.5 together
with the fact that by the condition of the lemma we have d(Vi, Vj) = w(i, j). The third inequality is
due to the fact that W has k vertices and thus |S| ≤ k2 .
The sought after contradiction now follows immediately from Propositions 4.4 and 4.6. This
completes the proof of the lemma.
We continue with the proof of Lemma 3.5.
14
Proof of Lemma 3.5: We prove the lemma with:
E3.5(r) =
� 1
16ɛ2 , r = 0
ɛ)), r ≥ 1
min( 1
8ɛr−2 , 1
8ɛ2 , γ3.4(r, 1
8
and
N3.5(r) = N3.4(r, 1
ɛ) .
8
We start with showing that EF(G) ≤ HF(W ) + ɛ. Suppose G is a graph of n vertices, in which
case the number of edges connecting Vi and Vj is w(i, j) · n2
k2 . We first remove all the edges within the
sets V1, . . . , Vk. As k ≥ 1/ɛ the total number of edges removed in this step is at most k � � n/k
2 ≤ ɛn2 .
Let S be the set of minimal weight whose removal turns W into an F-homomorphism-free graph
W ′ . We claim that if for every (i, j) ∈ S we remove all the edges connecting Vi and Vj the resulting
graph G ′ spans no copy of a graph F ∈ F. Suppose to the contrary that G ′ spans a copy of F ∈ F,
and consider the mapping ϕ : V (F ) ↦→ {1, . . . , k} that maps every vertex of F that belongs to Vj to
j. As we have removed all the edges within the sets V1, . . . , Vk and all edges between Vi and Vj for
any (i, j) ∈ S we get that ϕ is a homomorphism from F to W ′ contradicting our choice of S. Finally,
note that the number of edges removed in the second step is
�
(i,j)∈S
w(i, j) · n2
k 2 = n2 · HF(W ) .
Combined with the first step the total number of edges removed is at most n 2 · HF(W ) + ɛn 2 , as
needed.
For the rest of the proof we focus on proving HF(W ) ≤ EF(G) + ɛ. Let A and B be the two
equipartitions from the statement of the lemma. Suppose for every 1 ≤ i ≤ k we randomly, uniformly
and independently pick a set Vi,ti out of the sets Vi,1, . . . , Vi,l. Let P denote the event that (i) All
the pairs (Vi,ti , Vi ′ ,ti ′ ) are E(k)-regular. (ii) All but at most 1
2ɛ� � k
2 of the pairs (Vi,ti , Vi ′ ,ti ′ ) satisfy
|d(Vi,ti , Vi ′ ,ti ′ ) − d(Vi, Vi ′)| ≤ E(0). We need the following observations:
Proposition 4.7 P holds with probability at least 1 − 1
2 ɛ.
Proof: Fix any i < i ′ . By definition of E3.5 we have E(k) ≤ 1
8ɛk−2 , thus by item 1 of Definition
2.8, the probability that (Vi,ti , Vi ′ ,ti ′ ) is not E(k)-regular is at most 1
probability that one of the pairs is not E(k)-regular is at most � k
2
(5)
8ɛk−2 . By the union bound, the
�
1
8ɛk−2 ≤ 1
4ɛ. Item 2 of Definition 2.8 can be rephrased as stating that there are at most E(0) � � �
k
1 =
choices of i < i ′ for which the probability that |d(Vi,ti , Vi ′ ,ti ′ ) − d(Vi, Vi ′)| 1
> E(0) =
E(0) = 1
′)| > E(0) is at
most 1
� �
1
. By Markov’s inequality, the probability that more than
16ɛ2 . Thus, the expected number of i < i ′ for which |d(Vi,ti , Vi ′ ,ti ′ ) − d(Vi, Vi
16ɛ2� � � � k
k 1
2 · 1 + 2 · 16ɛ2 ≤ 1
8ɛ2� k
2
of i < i ′ violate the above inequality is at most ɛ
4 .
2
16ɛ2� k
2
16ɛ2 is larger than
As properties (i) and (ii) of event P each hold with probability at least 1 − 1ɛ,
we get that P
holds with probability at least 1 − 1
2 ɛ.
Proposition 4.8 Assume event P holds and denote by G ′ the subgraph of G that is spanned by the
sets V1,t1 , . . . , Vk,tk . Then, EF(G ′ ) ≥ HF(W ) − 1
2 ɛ.
15
4
2 ɛ� k
2
Proof: Let W ′ be a weighted complete graph on k vertices satisfying w(i, i ′ ) = d(Vi,ti , Vi ′ ,ti ′ ). Event
P assumes that all the pairs (Vi,ti , Vi ′ ,ti ′ ) are E(k)-regular. As E(k) ≤ γ3.4(k, 1
8ɛ) and the lemma
assumes that |Vi,j| ≥ N3.5(k) = N3.4(k, 1
8ɛ) we may deduce from Lemma 3.4 that
EF(G ′ ) ≥ HF(W ′ ) − ɛ
. (6)
8
Now, event P also assumes that all but at most ɛ
� � k
2 2 of the pairs i < i ′ are such that |d(Vi, Vi ′) −
d(Vi,ti , Vi ′ ,ti ′ )| ≤ E(0) < ɛ
8 . This means that the sum of edge weights of W ′ differs from the sum of
edge weights of W by at most ɛ
�
due to pairs that violate the above inequality and by at most
�k � � 2 2
k ɛ
2 8 due to the other pairs. This means that the sum of edge weights of W ′ differs from that of W
by at most ɛ
4k2 + ɛ
16k2 ≤ 3ɛ
8 k2 . This clearly implies that
The proof now follows by combining (6) and (7).
HF(W ′ ) ≥ HF(W ) − 3ɛ
8
. (7)
Let R be an arbitrary set of edges whose removal from G turns it into an F-free graph. Randomly
and uniformly select a set Vi,ti from each of the sets Vi,1, . . . , Vi,l, and let R ′ denote the set of edges
of R that are spanned by these k sets. We claim that the following upper and lower bound on the
expected size of R ′ hold:
1
l 2 · |R| = E[|R′ |]
≥ E[|R ′ | | P ] · P rob[P ]
≥ (1 − ɛ
2 ) · E[|R′ | | P ]
≥ (1 − ɛ
2 ) · (HF(W ) − ɛ
2
≥ (HF(W ) − ɛ) · n2
.
l2 ) · k2 n2
(kl) 2
Indeed, the equality is due to the fact than an edge of R has probability precisely 1/l 2 to be in
R ′ . The second inequality is due to Proposition 4.7, the third is due to Proposition 4.8 and the last
is valid because HF(W ) ≤ 1. As we thus infer that |R| ≥ HF(W ) · n 2 − ɛn 2 for arbitrary R, we get
that EF(G) ≥ HF(W ) − ɛ, thus completing the proof.
5 Proofs of Algorithmic Results
The technical lemmas proved in the previous sections enabled us to infer that certain E-regular
partitions may be very useful for approximating EP. In this section we apply Proposition 2.11 in
order to efficiently obtain these partitions. We first prove Theorem 1.1, while overlooking some subtle
issues. We then discuss them is detail.
Proof of Theorem 1.1: Fix any ɛ > 0 and monotone graph property P. Let F = FP be the family
of forbidden subgraphs of P as in Definition 3.1. As satisfying P is equivalent to being F-free, we
16
focus on approximating EF(G). Let E3.5(r) and N3.5(r) be the appropriate function with respect to
F and ɛ. Put S(ɛ) = S2.9(1/ɛ, E3.5) and recall that by Proposition 2.10 the integer S can indeed be
upper bounded by a function of ɛ.
If an input graph has less than S(ɛ) · N3.5(S(ɛ)) vertices we use exhaustive search in order
to precisely compute EF(G). Assume then that G has more than S(ɛ) · N3.5(S(ɛ)) vertices, and
use Proposition 2.11 with m = 1/ɛ and E3.5(r) as above in order to compute the equipartition
A = {Vi | 1 ≤ i ≤ k} and its refinement B = {Vi,j | 1 ≤ i ≤ k, 1 ≤ j ≤ l} satisfying the conditions
of Lemma 2.9. As m is bounded by a function of ɛ we get from Proposition 2.11 that this step takes
time O(n 2 ). Also, by Lemma 2.9 we have kl ≤ S, therefore, as G has at least S(ɛ)·N3.5(S(ɛ)) vertices
each of the sets Vi,j is of size at least N3.5(S(ɛ)) ≥ N3.5(k). Let W be a weighted complete graph of
size k where w(i, j) = d(Vi, Vj). Using exhaustive search, we can now precisely compute the value of
HF(W ). By Lemma 3.5 we may infer that |EF(G) − HF(W )| ≤ ɛ.
As we have mentioned in the introduction, one should specify how the property P is given to the
algorithm. For example, P may be an undecidable property, in which case we cannot do anything. We
thus focus on decidable graph properties. However, even in this case we may face some unexpected
problems. Note, that for a general infinite family of graphs F it is not clear how to compute HF
in finite time. Also, returning to the overview of the proof of Lemma 2.9 given in Section 2, note
that we have implicitly assumed that one can compute the function E, as this is needed in order to
compute the parameters with which one applies Lemma 2.10. A close inspection of the proofs of
Lemmas 3.4 and 3.5 reveals that computing E involves computing the function ΨF (see (2), (3) and
(5)). One of the main results of [5] asserts that somewhat surprisingly, there is a family of graph
properties F, for which the property of being F-free is decidable (in fact, in coNP ) but at the same
time ΨF is not computable. Therefore, even if we confine ourselves to decidable graph properties we
still run into trouble.
Suppose first that ɛ is not part of the input to the algorithm. As we have discussed in Section 2, in
this case all the applications of E3.5 are on inputs of size depending on ɛ only, thus the algorithm may
”keep” the answers to these (finitely many) applications of E3.5 as part of its description. Similarly, in
this case we may need to compute HF on graphs of size depending on ɛ only 5 , thus the algorithm may
”keep” the answers to these (finitely many) applications of HF as part of its description. Observe,
that we don’t need to keep the answer of HF for all the (infinite) range of edge weights. Rather, as
we only need to approximate EF within an additive error of ɛ, it is enough to consider edge weights
{0, ɛ, 2ɛ, 3ɛ, . . . , 1}.
If we want the algorithm to be able to accept ɛ as part of the input, then we must confine
ourselves to properties for which ΨF is computable. However, as for any reasonable graph property
this function is computable, this is not a real constraint. For example, as we have mentioned in
Section 4, if P is the property of being bipartite, then ΨF(k) is either k or k − 1. Another natural
family of properties for which ΨF(k) is computable is that of being H-free for a fixed graph H, as
in this case ΨF(k) ≤ |V (H)|. By the definition of the function E3.5 we get that if ΨF is computable
then so is E3.5. It is also not difficult to see that if ΨF is computable then so is HF. Therefore, in
case ΨF is computable, there is no problem with accepting ɛ as part of the input.
We now turn to prove Theorem 1.2. We note that the above difficulties are also relevant for
Corollary 1.2, which applies Theorem 1.2, but we refrain from discussing them again.
Proof of Theorem 1.2: (sketch) As in the previous proof, we focus on the property of being F-
5 Recall that the size of the graph on which we compute HF is the number of partition classes of the E-regular
partition, and this number is at most S2.9(m, E), which is bounded by a function of ɛ.
17
free, where F is the family of forbidden subgraphs of P. Suppose, as in the previous proof, that G is a
large enough graph (in terms of ɛ) as otherwise we can take D to be the entire vertex set of G. Assume,
we implicitly apply Lemma 2.9 on G and let A = {Vi | 1 ≤ i ≤ k}, B = {Vi,j | 1 ≤ i ≤ k, 1 ≤ j ≤ l}
be the equipartitions returned by the lemma. Let W be a weighted complete graph on k vertices,
where w(i, j) = d(Vi, Vj). By Lemma 3.5 we have
|EF(G) − HF(W )| ≤ ɛ . (8)
Let D be a random set of vertices and for 1 ≤ i ≤ k let Ui denote the vertices of D that belong
to Vi, and for 1 ≤ i ≤ k, 1 ≤ j ≤ l let Ui,j denote the vertices of D that belong to Vi,j. Recall
that k and l are bounded by functions of ɛ. Using standard Chernoff Bounds (see, e.g., [8]), it is
easy to see that if we use a large enough sample of vertices D (but only large enough in terms of
ɛ), then with high probability (whp) we will have |d(Vi, Vi ′) − d(Ui, Ui ′)| ≤ ɛ for any i < i′ and
|d(Vi,j, Vi ′ ,j ′) − d(Ui,j, Ui ′ ,j ′)| ≤ ɛ for any i < i′ and j �= j ′ . Therefore, if W ′ is a weighted complete
graph on k vertices, where w(i, j) = d(Ui, Uj) then
|HF(W ) − HF(W ′ )| ≤ ɛ . (9)
Furthermore, using Chernoff bounds again, one can show that whp all the pairs (Ui, Ui ′) and
(Ui,j, Ui ′ ,j ′) are as regular as (Vi, Vi ′) and (Vi,j, Vi ′ ,j ′) (up to ɛ). Therefore, the graph induced by D,
denoted G ′ , will have equipartitions A ′ , B ′ satisfying the requirements of Lemma 2.9. This means
that
|EF(G ′ ) − HF(W ′ )| ≤ ɛ . (10)
As (8), (9) and (10) all hold with high probability for any ɛ > 0, we can thus make sure that with
probability at least 1 − ɛ, we will have |EF(G ′ ) − EF(G)| ≤ ɛ. This completes the proof.
6 Overview of the Proof of Theorem 1.3
For the proof of Theorem 1.3 it will be more convenient to denote by E ′ P (G) the number of edge
removals needed to make G satisfy P, in other words E ′ P (G) = n2 · EP(G). In particular, E ′ H (G)
denotes the number of edge removals needed to turn G into an H-free graph. We will also denote
by E ′ r(G) the number of edge removals needed to turn G into an r-partite graph (or equivalently
r-colorable graph). Note, that approximating E ′ P (G) within n2−δ is equivalent to approximating
EP(G) within n−δ .
The main technical result we need in order to obtain Theorem 1.3 is an extension of some classical
results in Extremal Graph Theory. Recall, that Turán’s Theorem (see [45]) states that the largest
Kr+1-free graph on n vertices (Kr+1 = complete graph on r + 1 vertices) is precisely the largest
r-partite graph on n vertices. Another classical result is the Erdős-Stone-Simonovits Theorem (see
[45]), which states that for any graph H of chromatic number r + 1, the largest H-free graph on n
vertices has at most o(n2 ) more edges than the largest r-partite graph on n vertices. As any r-partite
graph does not contain a copy of a graph of chromatic number r + 1, the above results can thus be
restated as saying that when H = Kr+1 we have E ′ H (Kn) = E ′ r(Kn) and that for any H of chromatic
number r + 1 we have E ′ r(Kn) − o(n2 ) ≤ E ′ H (Kn) ≤ E ′ r(Kn).
The main extremal graph-theoretic tool that we use in order to obtain Theorem 1.3 is the following
result, which greatly extends one of the main results of [14]. Note, that this result also extends Turán’s
18
Theorem and the Erdős-Stone-Simonovits Theorem as it states that E ′ H (G) and E′ r(G) are very close
not only when G is Kn but already when G has a sufficiently large minimal degree.
Theorem 6.1 Let H be a graph of chromatic number r + 1 ≥ 3.
(i) If there is an edge of H whose removal reduces its chromatic number, then there is constant
µ = µ(H) > 0 such that if G = (V, E) is a graph on n vertices of minimum degree at least
(1 − µ)n, then E ′ H (G) = E′ r(G).
(ii) Otherwise, there are constants γ = γ(H) > 0 and µ = µ(H) > 0 such that if G = (V, E) is a
graph on n vertices of minimum degree at least (1 − µ)n, then
E ′ r(G) − O(n 2−γ ) ≤ E ′ H(G) ≤ E ′ r(G).
The assertion of this theorem for the special case of H being a triangle is proved in [14] and in
a stronger form in [15]. We note that the n2−γ term in the second item of the theorem cannot be
avoided. Note, that the error term we obtain in the second part of the theorem is better than the
error term of the classical Erdős-Stone-Simonovits Theorem. Such improvement of the error term
was previously known (see, e.g., [20] and [43]) but only for the case of G being Kn and not for G of
sufficiently high minimal degree. The proof of Theorem 6.1 appears in Section 7.
Our second tool in the proof of Theorem 1.3 is certain pseudo-random graphs. An (n, d, λ)-graph
is a d-regular graph on n vertices all of whose eigenvalues, except the first one, are at most λ in
their absolute values. This notation was introduced by the first author in the 80s, motivated by
the fact that if λ is much smaller than d, then such graphs have strong pseudo-random properties.
In particular, (see, e.g., [8], Chapter 9), in this case the number of edges between any two sets of
vertices U and W of G is roughly its expected value, which is |U||W |d/n, (see Section 8 for the
precise statement). There are many known explicit constructions of (n, d, λ)-graphs that suffice for
our purpose here. Specifically, we can use, for example, the graph constructed by Delsarte and
Goethals and by Turyn (see [37]). In this graph the vertex set V (G) consist of all elements of the
two dimensional vector space over GF (q) (q is any prime power), so G has n = q2 vertices. To define
the edges of G we fix a set L of k lines through the origin. Two vertices x and y of the graph G are
adjacent if x − y is parallel to a line in L. It is easy to check that this graph is d = k(q − 1)-regular.
Moreover, because it is a strongly regular graph, one can compute its eigenvalues precisely and show
that besides the first one they all are either −k or q − k. Therefore, by choosing k = (1 − µ) q2
q−1
we obtain an (n, d, λ)-graph with d = (1 − µ)n and λ ≤ √ n (µ will be chosen as the constant from
Theorem 6.1).
Given a graph F let Fb denote the b-blowup of F , that is, the graph obtained from F by replacing
every vertex v ∈ V (F ) with an independent set Iv, of size b, and by replacing every edge (u, v) ∈ E(F ),
with a complete bipartite graph, whose partition classes are the independent sets Iu and Iv. It is not
difficult to show (see Claim 8.2) that for any integer r, we have E ′ r(Fb) = b2E ′ r(F ). The final piece
of notation we need is the Boolean Or, denoted by G1 ∪ G2 of two graphs G1 and G2 on the same
set of vertices V . Its set of vertices is V , and its set of edges contains all edges of G1 and all edges
of G2.
Armed with these preparations, we can now outline the proof of Theorem 1.3. Its first part is
an easy application of Turán’s Theorem for bipartite graphs. The proof of the second part is more
interesting. Suppose all bipartite graphs satisfy P, and let r + 1 ( ≥ 3) be the minimum chromatic
number of a graph that does not satisfy this property. Fix a graph H of chromatic number r + 1
that does not satisfy P and let µ be the constant of Theorem 6.1. Consider, first, the case r ≥ 3.
19
In this case we show that any efficient algorithm that approximates E ′ P (G) up to n2−δ will enable
us to decide efficiently if a given input graph F = (V (F ), E(F )) is r-colorable. Indeed, given such
an F on m vertices, let b = m c where c is large constant, to be chosen appropriately. Let Fb be the
b-blowup of F , and let F ′ be the vertex disjoint union of r copies of Fb. Let G ′ be the (n, d, λ)-graph
with d = (1 − µ)n and λ ≤ √ n, whose number of vertices n, is at least the number of vertices of
F ′ , and not more than four times of that, and identify the vertices of F ′ with some of those of G ′ .
Let G = G ′ ∪ F ′ be the Boolean Or of these two graphs. If F is r-colorable, then so is its blowup
Fb, and hence in this case F ′ has a proper r-coloring in which all color classes have the same size.
This can be extended to a partition of the vertices of G to r nearly equal color classes, providing
an r-colorable subgraph of G (which satisfies P by our choice of r) that contains all edges of F ′ ,
and some edges of G ′ that do not belong to F ′ . The pseudo-random properties of G ′ enable us to
approximate this number well.
On the other hand, if F is not r-colorable, then any r-colorable subgraph of G misses at least b 2 r
edges of F ′ , and, by the pseudo-random properties of G ′ cannot contain too many edges of this graph
that do not belong to F ′ . With the right choice of c, this will ensure that if we can approximate
the number of edges in a maximum r-colorable subgraph of G up to an n 2−δ -additive error, this will
enable us to know for sure whether F is r-colorable or not. However, by Theorem 6.1, and as the
minimum degree of our graph is at least (1 − µ)n, the maximum size of an H-free subgraph of G is
very close to the maximum size of an r-colorable subgraph of it, which is therefore also very close
to the maximum number of edges in a subgraph of G satisfying P. This implies that approximating
well this last quantity is NP -hard. The case r = 2 is similar, but here we have to use that the
MAX-CUT problem is NP -hard. The full details appear in Section 8.
7 Proof of Theorem 6.1
Throughout this section we will assume that the number of vertices n in our graph is sufficiently
large. We first prove the first part of Theorem 6.1, which is an extension of Turán’s theorem. To
this end, we need a result proved for Kr+1-free graphs by Andrásfai, Erdős and Sós [9] and in a more
general form by Erdős and Simonovits [21].
Theorem 7.1 ([9],[21]) Let H be a fixed graph with chromatic number r + 1 ≥ 3 which contains an
edge e such that χ(H − e) = r. If G is an H-free graph of order n with minimal degree δ(G) > 3r−4
3r−1 n
then G is r-colorable.
We will also need the following simple lemma.
Lemma 7.1 Let r ≥ 2 be an integer and suppose G ′ is an r-partite subgraph of a graph G (which
may be empty) such that there are m edges incident to the vertices in V (G)\V (G ′ ). Then G has an
r-partite subgraph of size at least e(G ′ ) + r−1
r m.
Proof: Let (A ′ 1 , . . . , A′ r) be the partition of G ′ . Consider an r-partite subgraph Γ of G with parts
(A1, . . . , Ar) such that A ′ i ⊂ Ai for every i, where we place each vertex v ∈ V (G)\V (G ′ ) in Ai
randomly and independently with probability 1/r. All edges of G ′ are edges of Γ, and each edge
incident to a vertex in V (G)\V (G ′ ) appears in Γ with probability r−1
r . By linearity of expectation
E � e(Γ) � = e(G ′ ) + r−1
r m, so some r-partite subgraph of G has at least this many edges.
In particular, by taking G ′ to be the empty graph we obtain that every G contains an r-partite
subgraph of size at least r−1
r e(G).
20
Proof of Theorem 6.1 part (i): We prove that E ′ H (G) = E′ r(G) for all graphs G on n vertices
with minimum degree
δ(G) ≥
�
�
3
1 −
n + 1.
4(r − 1)(3r − 1)
Let Γ be the largest (in terms of number of edges) r-partite subgraph of G and let F be the largest
H-free subgraph of G. To prove the first part of the theorem one needs to show that e(F ) = e(Γ).
As H is not r-colorable we trivially have e(F ) ≥ e(Γ). In the rest of the proof we establish that
e(Γ) ≥ e(F ). First, note that by Lemma 7.1 we have
e(Γ) ≥
If F has a vertex of degree at most 3r−4
3r−1
= r − 1
r − 1
e(G)
r
�� 3
� �
1 −
n + 1 n/2
r
4(r − 1)(3r − 1)
= 12r2 − 16r + 1
8r(3r − 1) n2 r − 1
+
2r n.
n we delete it and continue. We construct a sequence of
graphs F = Fn, Fn−1, ..., where if Fk has a vertex of degree ≤ 3r−4k
we delete that vertex to obtain
3r−1
Fk−1. Let F ′ be the final graph of this sequence which has s vertices and minimal degree greater
than 3r−4
3r−1s. Since F ′ is H-free, by Theorem 7.1, it is r-partite. Therefore we have that
r − 1
2r s2 ≥ e(F ′ 3r − 4
) ≥ e(F ) −
3r − 1
≥ e(Γ) −
�� n + 1
2
�
−
3r − 4
2(3r − 1) (n2 − s 2 3r − 4
) −
2(3r − 1) n
≥ 12r2 − 16r + 1
8r(3r − 1) n2 3r − 4
−
2(3r − 1) (n2 − s 2 ).
� ��
s + 1
s This implies that
2
2r(3r−1) ≥ n2
8r(3r−1) and so s ≥ n/2.
Let X be the set of n − s vertices which we deleted, i.e., X = V (G) − V (F ′ ). By the minimal
degree assumption there are at least
� �
|X|
m ≥ δ(G)|X| −
≥
2
12r2 − 16r + 1
(n − s)2
n(n − s) + (n − s) −
4(r − 1)(3r − 1) 2
edges incident with vertices in X. Thus, by Lemma 7.1, the size of the largest r-partite subgraph of
G is at least
e(Γ) ≥ e(F ′ �� � � ��
r − 1
3r − 4 n + 1
s + 1
) + m ≥ e(F ) − −
+
r 3r − 1
2
2
r − 1
r m
=
3r − 4
e(F ) −
2(3r − 1) (n2 − s 2 3r − 4
r − 1
) − (n − s) +
2(3r − 1) r m
≥
3r − 4
e(F ) −
2(3r − 1) (n2 − s 2 � �
r − 1 12r2 − 16r + 1
(n − s)2
) + n(n − s) −
r 4(r − 1)(3r − 1) 2
=
(n − s)(2s − n)
e(F ) + ≥ e(F ).
4r(3r − 1)
This implies that e(Γ) ≥ e(F ) and completes the proof.
21
2
We turn to prove Theorem 6.1 part (ii). To this end, we first prove the main technical result of
this section, Theorem 7.2 below, which is a version of Theorem 7.1 that applies to arbitrary graphs
H. We then apply this theorem in order to prove Theorem 6.1 part (ii). The reader may want to
note that this application of Theorem 7.2 is similar to the way we applied Theorem 7.1 in order to
prove the first part of Theorem 6.1.
Theorem 7.2 Let H be a fixed graph on h vertices with chromatic number r +1 ≥ 3 and let G be an
H-free graph of order n with minimum degree δ(G) ≥ � r−1
1
r − 3hr2 �
n. Then one can delete at most
O � n2−(r+1)/h� edges to make G r-colorable.
Proof: First we need the following weaker bound on E ′ r(G).
Claim 7.2 G can be made r-partite by deleting o(n 2 ) edges.
Proof: We use the Regularity Lemma given in Lemma 2.6. For every constant 0 < η < 1
12hr 2 let
γ = γ2.3(η, r + 1, h) < η 2 be sufficiently small to guarantee that the assertion of Lemma 2.3 holds 6 .
Consider a γ-regular partition (U1, U2, . . . Uk) of G. Let G ′ be a new graph on the vertices 1 ≤ i ≤ k
in which (i, j) is an edge iff (Ui, Uj) is a γ-regular pair with density at least η. Since G is an H-free
graph and H is homomorphic to Kr+1 (as χ(H) = r +1), by Lemma 2.3, G ′ contains no clique of size
r + 1. Call a vertex of G ′ good if there are at most ηk other vertices j such that the pair (Ui, Uj) is
not γ-regular, otherwise call it bad. Since the number of non-regular pairs is at most γ � � k
2 ≤ η2k2 /2
we have that all but at most ηk vertices are good. By definition, the degree of each good vertex in
G ′ is at least � r−1
1
r − 3hr2 �
k − 2ηk − 1, since deletion of the edges from non-regular pairs and sparse
pairs can decrease the degree by at most ηk each and the deletion of edges inside the sets Ui can
decrease it by 1. By deleting all bad vertices we obtain a Kr+1-free graph on at most k vertices with
minimal degree at least
�
r − 1 1
−
r 3hr2 �
�
r − 1 2
k − 3ηk − 1 ≥
−
r 3hr2 �
k
�
r − 1 1
≥
−
r 3r2 �
k
> 3r − 4
3r − 1 k.
Therefore, by Theorem 7.1, this graph is r-partite. This implies that to make G r-partite we can
delete at most γn 2 + ηn 2 + (ηn) · n + k · (n/k) 2 ≤ 3ηn 2 + n 2 /k = o(n 2 ) edges.
Consider a partition (V1, . . . , Vr) of the vertices of G into r parts which maximizes the number
of crossing edges between the parts. Then for every x ∈ Vi and j �= i the number of neighbors of x
in Vi is at most the number of its neighbors in Vj, as otherwise by shifting x to Vj we increase the
number of crossing edges. By Claim 7.2, we have that this partition satisfies that �
i e(Vi) = o(n2 ).
Call a vertex x of G typical if x ∈ Vi and has at most n/(10hr2 ) neighbors in Vi. Note that there
are at most o(n) non-typical vertices in G and, in particular, every part Vi contains a typical vertex.
By definition, the degree of this vertex outside Vi is at least � r−1
1
r − 3hr2 �
n
n − 10hr2 > � r−1
1
r − 2hr2 �
n
and at most n − |Vi|. Therefore |Vi| ≤ ( 1
1
r + 2hr2 )n. Also note that the number of neighbors in Vi of
6 Recall that by Comment 2.4 we may assume that γ2.3(η, r + 1, h) < η 2 .
22
every typical vertex x ∈ Vj, j �= i is at least
(x) ≥ d(x) − dVj (x) − (r − 2) max |Vk|
k
�
r − 1 1
≥
−
r 3hr2 �
n − n
�
1 1
− (r − 2) +
10hr2 r 2hr2 �
n
�
1 r − 1
>
−
r 2hr2 �
n. (11)
dVi
The next claim is an immediate corollary of the above observation.
Claim 7.3 Let U be a subset of Vj of size at least ( 1
1
2r − 4hr )n and let y1, . . . , yk be an arbitrary set
n
of k ≤ r − 1 typical vertices outside Vj. Then, there are at least 2r(r+1) vertices in U, which are
adjacent to all vertices yi.
Proof: By definition, there are at most |Vj| − dVj (yi) non-neighbors of yi in Vj and thus there are at
most that many vertices in U not adjacent to yi. Delete from U any vertex, which is not a neighbor
of either y1, y2, . . . , yk. The remaining set is adjacent to every vertex yi and has size at least
|U| − � �
|Vj| − dVj (yi) � .
i
Since by (11) the degree in Vj of every typical vertex yi �∈ Vj is at least dVj (yi) ≥ ( 1 r−1
r − 2hr2 )n, we
obtain that the number of common neighbors of y1, . . . , yk in U is at least
|U| − � �
|Vj| − dVj
i
(yi) � ≥
�
1 r − 1
k −
r 2hr2 ≥
�
n − k|Vj| + |U|
�
1 r − 1
k −
r 2hr2 �
�
1 1
n − k +
r 2hr2 ≥
�
n + |U|
|U| − k
� �
1 1
n ≥ − n −
2hr 2r 4hr
k
2hr n
≥
� �
1 k + 1
− n ≥
2r 2hr
n n
−
2r 2h ≥
n
2r(r + 1) .
Here we used that k + 1 ≤ r and h ≥ r + 1.
Claim 7.4 For every non-typical vertex x ∈ Vi there are at least
that yj ∈ Vj for all 1 ≤ j ≤ r and all vertices yj are adjacent to x.
n r
5h(3r 2 ) r r-cliques y1, . . . , yr such
Proof: Without loss of generality let i = 1 and let x ∈ V1 be a non-typical vertex. Since for every
j �= 1 the number of neighbors of x in Vj is at least as large as the number of its neighbors in V1 we
have that
≥
2
1
��r − 1 1
−
2
r 3hr2 �
�
n − (r − 2) max |Vi|
i
≥ 1
��r − 1 1
−
2
r 3hr2 �
�
1 1
n − (r − 2) +
r 2hr2 � �
n
� �
1 1
≥
− n. (12)
2r 4hr
dVj (x) + dV1 (x)
dVj (x) ≥
23
To construct the r-cliques satisfying the assertion of the claim, first observe, that since x is
non-typical it has at least n/(10hr 2 ) neighbors in V1 and at least n/(10hr 2 ) − o(n) > n/(15hr 2 ) of
these neighbors are typical. Choose y1 to be an arbitrary typical neighbor of x in V1 and continue.
Suppose at step 1 ≤ k ≤ r − 1 we already have a k-clique y1, . . . , yk such that yi ∈ Vi for all i and all
vertices yi are adjacent to x. Let Uk+1 be the set of neighbors of x in Vk+1. Then, by (12) we have
1
1
n
that |Uk+1| = dVk+1 (x) ≥ ( 2r − 4hr )n and therefore by Claim 7.3 there are at least 2r(r+1) common
n
n
neighbors of the vertices yi in Uk+1. Moreover, at least 2r(r+1) − o(n) > 3r2 of them are typical and
we can choose yk+1 to be any of them. Therefore at the end of the process we indeed obtained at
least n
15hr 2 ( n
3r 2 ) r−1 = nr
5h(3r 2 ) r r-cliques with the desired property.
Claim 7.5 Each Vi contains at most O(1) non-typical vertices.
Proof: Suppose that the number of non-typical vertices in Vi is at least 5h2 (3r2 ) r . Consider an
auxiliary bipartite graph F with parts W1, W2, where W1 is the set of some t = 5h2 (3r2 ) r non-typical
vertices in Vi, W2 is the family of all nr r-element multi-sets of V (G) such that x ∈ W1 is adjacent
to multi-set Y from W2 iff Y is an r-clique in G with exactly one vertex in every Vj and all vertices
n of Y are adjacent to x. By the previous claim, F has at least e(F ) ≥ t r
5h(3r2 ) r = hnr edges and
therefore the average degree of a vertex in W2 is at least dav = e(F )/|W2| = e(F )/nr ≥ h. By the
convexity of the function f(z) = � � z
h , we can find h vertices x1, . . . , xh in W1 such that the number
of their common neighbors in W2 is at least
m ≥
�
�d(Y )
Y ∈W2 h
�t h
�
� ≥ n r
� � dav
h
th = Ω�n r� .
Thus we proved that G contains h vertices X = {x1, . . . , xh} and a family of r-cliques C of size
m = Ω � n r� such that every clique in C is adjacent to all vertices in X. Next we need the following
well-known lemma which appears first implicitly in Erdős [19] (see also, e.g., [27]). It states that if
an r-uniform hypergraph on n vertices has m = Ω � n r� edges, then it contains a complete r-partite
r-uniform hypergraph with parts of size h. By applying this statement to C, we conclude that there
are r disjoint set of vertices A1, . . . , Ar each of size h such that every r-tuple a1, . . . , ar with ai ∈ Ai
forms a clique which is adjacent to all vertices in X. The restriction of G to X, A1, . . . , Ar forms a
complete (r +1)-partite graph with parts of size h each, which clearly contains H. This contradiction
shows that there are less than 5h 2 (3r 2 ) r = O(1) non-typical vertices in Vi and completes the proof
of the claim.
Having finished all the necessary preparations, we are now ready to complete the proof of Theorem
7.2. Let h1 ≤ h2 ≤ . . . ≤ hr+1 be the sizes of the color-classes in an r + 1 coloring of H. Clearly
h1 ≤ h/(r + 1). Without loss of generality, suppose that V1 spans at least 2hn 2−(r+1)/h edges. By
the previous claim, only at most O(n) of these edges are incident to non-typical vertices. Therefore
the set of typical vertices in V1 spans at least hn 2−(r+1)/h edges. Then, by the well known result
of Kövari, Sós and Turán [36] about Turán numbers of bipartite graphs, V1 contains a complete
bipartite graph H1 = Kh1,h2 all of whose vertices are typical. If there are at least h3 typical vertices
in V2 which are adjacent to all vertices of H1 then we add them to H1 to form a complete 3-partite
graph H2 with parts of sizes h1, h2 and h3 and continue. We claim that if at step 1 ≤ k ≤ r − 1 there
is a k + 1-partite graph Hk ⊂ ∪ k i=1 Vi with parts of sizes h1, . . . , hk+1 all of whose vertices are typical,
then we can extend it to the complete k + 2-partite graph Hk+1 by adding hk+2 typical vertices from
Vk+1 which are adjacent to all vertices of Hk. Indeed, recall that by (11) the number of neighbors
24
1 r−1
in Vk+1 of every typical vertex x ∈ Vi, i �= k + 1 is at least dVk+1 (x) ≥ ( r − 2hr2 )n. Let t ≤ h be the
order of Hk. Then, as in Claim 7.3 the number of vertices in Vk+1 which are adjacent to all vertices
of Hk is at least
�
�
1 r − 1
|Vk+1| − t |Vk+1| − −
r 2hr2 � �
n ≥
�
1 r − 1
t −
r 2hr2 �
�
1 1
n − (t − 1) +
r 2hr2 �
n
= n t(r − 1) + t − 1
−
r 2hr2 ≥
n
n t
n n n
− n ≥ − =
r 2hr r 2r 2r
and thus at least n/(2r) − O(1) > hk+2 of these vertices are typical. Continuing the above process
r − 1 steps we obtain a complete (r + 1)-partite graph with parts of sizes h1, . . . , hr+1, which clearly
contains H. This contradicts our assumption that G is H-free and shows that every Vi spans at
most O � n 2−(r+1)/h� edges. Therefore the number of edges we need to delete to make G r-partite is
bounded by �
i e(Vi) ≤ O � n 2−(r+1)/h� . This completes the proof.
Proof of Theorem 6.1 part (ii): Let H be a fixed graph on h vertices with chromatic number
r + 1 ≥ 3. We show that the constants γ(H) and µ(H) in the assertion of the theorem can be
chosen to be (r + 1)/h and 1/(4hr2 ) respectively. Let G be an H-free graph of order n with minimal
degree δ(G) ≥ (1 − 1
4hr2 )n and let Γ be the largest r-partite subgraph of G and F be a largest H-free
subgraph of G. To prove the second item of the theorem it is enough to show that e(Γ) ≤ e(F ) ≤
e(Γ) + O(n2−(r+1)/h ). As H is not r-colorable we trivially have e(Γ) ≤ e(F ). In the rest of the proof
we establish that e(F ) ≤ e(Γ) + O(n2−(r+1)/h ). By Lemma 7.1 we have that
e(Γ) ≥
r − 1
r − 1
e(G) =
r
r
�
1 − 1
4hr 2
�
n 2 /2 =
� r − 1
2r
− r − 1
8hr 3
�
n 2 .
If F has a vertex of degree at most ( r−1
1
r − 3hr2 )n we delete it and continue. We construct a sequence
of graphs F = Fn, Fn−1, ..., where if Fk has a vertex of degree ≤ ( r−1
1
r − 3hr2 )k we delete that vertex
to obtain Fk−1. Let F ′ be the final graph of this sequence which has s vertices and minimal degree
greater than ( r−1
r
− 1
3hr 2 )s and let Γ ′ be the largest r-partite subgraph of F ′ . Since F ′ is H-free,
Theorem 7.2 implies e(F ′ ) ≤ e(Γ ′ ) + O(n 2−(r+1)/h ). Therefore we have that
r − 1
This implies that
and so s ≥ n/2.
�
r − 1 1
−
r 3hr2 � �� � � ��
n + 1
s + 1
−
2
2
�
r − 1 1
≥ e(Γ) −
−
2r 6hr2 �
(n 2 − s 2 ) − O(n)
�
r − 1 r − 1
≥
−
2r 8hr3 �
n 2 �
r − 1 1
−
−
2r 6hr2 �
(n 2 − s 2 ) − o(n 2 ).
2r s2 + o(n 2 ) ≥ e(F ′ ) ≥ e(F ) −
s2 ≥
6hr2 �
1 r − 1
−
6hr2 8hr3 �
n 2 − o(n 2 �
1
1
) >
−
6hr2 8hr2 �
n 2 = n2
24hr2 25
Let X be the set of n − s vertices which we deleted, i.e., X = V (G) − V (F ′ ). By the minimal
degree assumption there are at least
� �
�
|X|
m ≥ δ(G)|X| −
≥ 1 −
2
1
4hr2 �
(n − s)2
n(n − s) −
2
��1 1
= (n − s) −
2 4hr2 �
n + s
�
2
edges incident with vertices in X. Thus, by Lemma 7.1, the size of the largest r-partite subgraph of
G is at least
e(Γ) ≥ e(Γ ′ r − 1
) +
r m ≥ e(F ′ ) − O � n 2−(r+1)/h� r − 1
+
r m
�
r − 1 1
≥ e(F ) −
−
r 3hr2 � �� � � ��
n + 1
s + 1
−
+
2
2
r − 1
r m − O�n 2−(r+1)/h�
�
r − 1 1
≥ e(F ) −
−
2r 6hr2 �
(n 2 − s 2 r − 1
) +
r m − O�n 2−(r+1)/h�
�
r − 1 1
≥ e(F ) −
−
2r 6hr2 �
(n 2 − s 2 ��r − 1 r − 1
) + (n − s)
−
2r 4hr3 �
�
(r − 1)s
n + − O
2r
� n
r−3
(n − s)(2s − r = e(F ) + n)
12hr2 − O � n 2−(r+1)/h� ≥ e(F ) − O � n 2−(r+1)/h� .
8 Proof of Theorem 1.3
r+1
2− h
We start with the proof of the first part of Theorem 1.3. If there is a bipartite graph H that does
not satisfy P, then, by the known results about the Turán numbers of bipartite graphs proved in
[36], there exists a positive δ > 0 such that for any large n, any graph with n vertices and at least
n 2−δ edges contains a copy of H. Thus, given a graph G on n vertices, one must delete all its edges
besides, possibly, n 2−δ of them, to obtain a subgraph satisfying P. As certainly the edgeless graph
satisfies P, this provides the required approximation in this case.
The proof of the second part is more complicated, and requires all the preparations obtained
in the previous section. Suppose all bipartite graphs satisfy P, and let r + 1 ≥ 3 be the minimum
chromatic number of a graph that does not satisfy this property. Fix a graph H of chromatic number
r + 1 that does not satisfy P. We will show that any efficient algorithm that approximates E ′ P (G)
up to n 2−δ will enable us to decide efficiently how many edges we need to delete from a given input
graph F = (V (F ), E(F )) to make it r-partite. For r ≥ 3 this problem contains the r-colorability
problem, and for r = 2 it is the MAX-CUT problem and therefore it is NP -hard for every r ≥ 2.
Given a graph F on m vertices such that we need to delete ℓ edges to make it r-partite, let
b = m c where c is a large constant, to be chosen later. Let Fb be the b-blowup of F , and let F ′ be
the vertex disjoint union of r copies of Fb. Let µ = µ(H) be the constant from Theorem 6.1 and let
G ′ be the (n, d, λ)-graph with d = (1 − µ)n and λ ≤ √ n, described in Section 6. As the integer q in
the construction discussed in Section 6 can be a prime power, we can always choose the number of
vertices of G ′ , which is q 2 , to be at least the number of vertices of F ′ , and not more than 4 times of
that. In particular, we have n = Θ(rmb) = Θ � m c+1� . Identify the vertices of F ′ with some of those
of G ′ . Let G = G ′ ∪ F ′ be the Boolean Or of these two graphs.
Suppose, that instead of adding to F ′ a pseudo-random graph G ′ , we would put any non-edge
of F ′ in G with probability 1 − µ. It is easy to see that in this case the expected number of edges,
26
�
which would be spanned by a set of a vertices that span t edges in F ′ , would be (1 − µ) � � a
2 + µt.
The following claim establishes that this is approximately what we find when we add to F ′ a pseudorandom
graph. We then use this claim to show that we can also estimate E ′ r(G) as a function of
ℓ = E ′ r(F ).
Claim 8.1 Let A be a subset of the vertices of G of size a which contains precisely t edges of F ′ .
Then the number of edges of G in A satisfies
(1 − µ) a2
2 + µt − O� m 2 n 3/2� ≤ eG(A) ≤ (1 − µ) a2
2 + µt + O� m 2 n 3/2� .
Proof: By construction, the edges of the subgraph of F ′ induced on the set A form an edge disjoint
union of complete bipartite graphs we denote by Γi = (Ui, Wi), 1 ≤ i ≤ k. Thus �
i |Ui|Wi| = t and
the fact that F ′ is a blowup of r disjoint copies of F , which altogether have rm vertices and at most
r � � � � m
m
2 edges, implies that k ≤ r 2 < rm2 . The number of edges of G spanned on A is the number of
edges of G ′ inside A, minus the number of edges of G ′ spanned by the pairs (Ui, Wi), plus the number
of edges of F ′ inside A. To estimate this quantity, we need the well-known fact (see, e.g, Chapter 9
of [8]), that the number of edges between two subsets X, Y of an (n, d, λ)-graph G ′ satisfies
�
�
�
�
|X||Y |d �
� e(X, Y ) − �
n � ≤ λ� |X||Y |
and the fact that in such a graph � � d|X|
e(X) − 2 �
� ≤ λ|X|. Therefore we obtain that
eG(A) = eG ′(A) −
≥ d|A|2
2n
≥ d|A|2
2n
= (1 − µ) a2
2
k�
i=1
− λ|A| +
− λn +
+ µ
2n
eG ′(Ui, Wi) + t = eG ′(A) +
k�
i=1
k�
i=1
�
|Ui|Wi| − d
n |Ui|Wi| − λ � �
|Ui|Wi|
k� �
µ|Ui|Wi| − λn �
i=1
k�
|Ui|Wi| − (k + 1)λn
i=1
= (1 − µ) a2
2 + µt − O� m 2 n 3/2� .
�
|Ui|Wi| − eG ′(Ui,
�
Wi)
The upper bound eG(A) ≤ (1 − µ) a2
2 + µt + O� m 2 n 3/2� can be obtained similarly.
Recall that the b-blowup Fb of a graph F , defined in Section 6, is the graph obtained from F by
replacing every vertex v ∈ V (F ) with an independent set Iv, of size b, and by replacing every edge
(u, v) ∈ E(F ), with a complete bipartite graph, whose partition classes are the independent sets Iu
and Iv.
Claim 8.2 For any graph F and any integer b, we have E ′ r(Fb) = b 2 E ′ r(F ).
27
Proof: We start by showing that E ′ r(Fb) ≤ b 2 E ′ r(F ). Suppose S is a set of E ′ r(F ) edges whose
removal turns F into an r-colorable graph F ′ . Suppose we remove from Fb all the edges connecting
Iu and Iv for any (u, v) ∈ S. Note, that we thus remove b 2 E ′ r(F ) edges from Fb. We claim that the
resulting graph F ′ b is r-colorable. Indeed, let c : V (F ) ↦→ {1, . . . , r} be a r-coloring of F ′ and note
that by definition of F ′ b , if we color all the vertices of Iv with the color c(v), we get a legal r-coloring
of F ′ . Therefore E ′ r(Fb) ≤ b 2 E ′ r(F ).
To see that E ′ r(Fb) ≥ b 2 E ′ r(F ), let S be a set of edges whose removal turns Fb into an r-colorable
graph, and suppose for every v ∈ V (F ) we randomly pick a single vertex from each of the sets Iv. For
every edge of S, the probability that we picked both of its endpoints is b −2 , therefore the expected
number of edges spanned by these vertices is |S|/b 2 . As the removal of the edges of S makes Fb
r-colorable, this in particular applies to all of its subgraphs. Note, that for any choice of a single
vertex from each of the independent sets Iv, the graph they span is isomorphic to F . Thus, any such
choice spans at least E ′ r(F ) of the edges of S. It thus must be the case that |S|/b 2 ≥ E ′ r(Fb), and
the proof is complete.
Claim 8.3 The graph G satisfies
�
�
�
�E′ r(G) −
�
(1 − µ) n2
2r + µrℓb2�� � �� ≤ O(m 2 n 3 ). (13)
Proof: Fix a partition of F into r parts which misses exactly ℓ edges and consider r disjoint copies
of F . By taking appropriately different parts in every copy of F we can partition this new graph
into r equal parts such that exactly rℓ edges are non-crossing. Since F ′ is a b-blowup of r disjoint
copies of F , this gives a partition of F ′ into equal parts which misses rℓb2 edges. We can extend this
to a partition of G into r nearly equal sets V (G) = V1 ∪ . . . ∪ Vr which misses exactly rℓb2 edges of
F ′ . Let ti be the number of edges of F ′ inside Vi, then �
i ti = rℓb2 . This, together with Claim 8.1,
implies that it is enough to delete at most
r�
eG(Vi) ≤
i=1
r�
i=1
�
|Vi| 2
(1 − µ)
2 + µti + O � m 2 n 3/2��
(n/r + 1)2
≤ (1 − µ)r + µ
2
r�
ti + O � m 2 n 3/2�
i=1
= (1 − µ) n2
2r + µrℓb2 + O � m 2 n 3/2� .
edges to make G r-partite and hence to satisfy property P.
On the other hand, by Claim 8.2, any partition of F ′ , which is b-blowup of r disjoint copies of F ,
into r parts misses at least rℓb 2 edges. Therefore for every partition of the vertices of G into r sets
there are at least rℓb 2 edges of F ′ which are non-crossing. Let V1 ∪ . . . ∪ Vr be a partition of V (G)
that maximizes the number of crossing edges and let again ti be the number of edges of F ′ inside Vi
(note that in this case the sets Vi are not necessarily of the same size). Using Claim 8.1, together
with the fact that �
i ti ≥ rℓb 2 and the Cauchy-Schwartz inequality, we conclude that
28
r�
eG(Vi) ≥
i=1
This completes the proof of the claim.
≥
r�
i=1
�
|Vi| 2
(1 − µ)
2 + µti − O � m 2 n 3/2��
1 − µ
r
2
��
i |Vi|
r
� 2
+ µrℓb 2 − O � m 2 n 3/2�
= (1 − µ) n2
2r + µrℓb2 − O � m 2 n 3/2� .
We are now ready to complete the proof of Theorem 1.3. Choose the constant c to be sufficiently
large so that 2/(c + 1) < min(δ, γ, 1/4). Recall, that as we chose b = m c and n = Θ(m c+1 ), we have
n 2−δ = o(b 2 ), n 2−γ = o(b 2 ), m 2 n 3/2 = o(b 2 ). (14)
Also, as G has minimum degree (1 − µ)n we get from Theorem 6.1, that
E ′ H(G) ≥ E ′ r(G) − O(n 2−γ ). (15)
As H does not satisfy P we clearly have E ′ P (G) ≥ E′ H (G). Combining this with (13), (14) and (15)
we get
E ′ P(G) ≥ E ′ H(G) ≥ E ′ r(G) − O(n 2−γ )
≥ (1 − µ) n2
2r + µrℓb2 − O � m 2 n 3/2� − O � n 2−γ�
≥ (1 − µ) n2
2r + µrℓb2 − o � b 2� .
Furthermore, by our choice of r, we get that any r-colorable graph satisfies P, hence we infer from
(13) and (14) that
E ′ P(G) ≤ E ′ r(G) ≤ (1 − µ) n2
2r + µrℓb2 + O � m 2 n 3/2�
≤ (1 − µ) n2
2r + µrℓb2 + o � b 2� .
We thus conclude that |E ′ n2
P (G) − ((1 − µ) 2r + µrℓb2 )| ≤ o(b2 ). Therefore, if one can approximate
E ′ P (G) in time polynomial in n (and hence also in m) within an additive error of n2−δ = o(b2 )
then one thus efficiently computes an integer L, which is within an additive error of o(b2 ) from
(1 − µ) n2
2r + µrℓb2 . But as in this case ℓ is precisely the nearest integer to (L − (1 − µ) n2
2r )/µrb2 ,
this implies that we can precisely compute the number of edge removals, needed in order to turn
the input graph F into an r-partite graph. This implies that the problem of approximating E ′ P (G)
within n 2−δ is NP -hard, and completes the proof of Theorem 1.3.
29
9 Concluding Remarks and Open Problems
• We have shown that for any monotone graph property P and any ɛ > 0 one can approximate
efficiently the minimum number of edges that have to be deleted from an n-vertex input graph
to get a graph that satisfies P, up to an additive error of ɛn 2 . Moreover, for any dense monotone
property, that is, a property for which there are graphs on n vertices with Ω(n 2 ) edges that
satisfy it, it is NP -hard to approximate this minimum up to an additive error of n 2−δ . It will
be interesting to obtain similar sharp results for the case of sparse monotone properties. In
some of these cases (like the property of containing no cycle, or the property of containing no
vertex of degree at least 2) the above minimum can be computed precisely in polynomial time,
and in some other cases, a few of which are treated in [12], [13], [46], a precise computation is
known to be hard. Obtaining sharp estimates for the best approximation achievable efficiently
seems difficult.
• As we have mentioned in Section 1, a special case of Theorem 1.3 implies that for any nonbipartite
H, computing the smallest number of edge removals that are needed to make a graph
H-free is NP -hard. This is clearly not the case for some bipartite graphs such as a single edge
or any star. It will be interesting to classify the bipartite graphs for which this problem is
NP -hard.
• It seems interesting to decide if one can obtain a result analogous to Theorem 1.3 for the family
of hereditary properties.
• A weaker version of Theorem 1.1 can be derived by combining the results of [7] and [24].
However, this only enables one to approximate EP(G) within an additive error ɛ in time n f(ɛ) ,
while the running time of our algorithm is of type f(ɛ)n 2 .
• Recall that E ′ F (G) denotes the smallest number of edge deletions that are needed in order to
make G F-free. For a family of graphs F, let νF(G) denote the F-packing number of G, which
is the size of the largest family of edge-disjoint copies of members of F, which is spanned by
G. Let ν∗ F (G) denote the natural Linear Programming relaxation of νF(G). Haxell and Rödl
[32] and Yuster [47] have shown that νF(G) ≤ ν∗ F (G) ≤ νF(G) + ɛn2 for any F and any ɛ > 0,
implying that for any finite F , νF(G) can be approximated within any additive error of ɛn2 by solving the Linear Program for computing ν∗ F (G). One may wonder whether it is possible
to obtain Theorem 1.1 by solving the natural Linear Programming relaxation of E ′ F (G), which
we denote by E∗ F (G). Regretfully, this is not the case. Linear Programming duality implies
(G) and by the results of [32] and [47] we thus have
that E ∗ F (G) = ν∗ F
νF(G) ≤ E ∗ F(G) ≤ νF(G) + ɛn 2 . (16)
Consider now any F, which does not contain the single edge graph and note that we trivially
have νF(Kn) ≤ 1
� � n
1
2 2 ≤ 4n2 (we denote by Kn the n-vertex complete graph). If F contains
a bipartite graph then by the theorem of Kövari, Sós and Turán (see Section 6) we have
E ′ F (Kn) > � � n
2 − n2−δ 1 ≥ ( 2 − o(1))n2 . If on the other hand all the graphs in F are of chromatic
number r ≥ 3 then clearly they all must contain at least � � r
2 edges, and therefore we must have
νF(Kn) ≤ � � � � n r
n
2 / 2 ≤ 2
. On the other hand, by the theorem of Erdős-Stone-Simonovits
r(r−1)
(see Section 6) E ′ F (Kn) > n2
2(r−1) − o(n2 ). In any case, we have that νF(Kn) + δn2 ≤ E ′ F (Kn)
for some fixed δ = δ(F) > 0. Combined with (16) we get that for any F not containing the
30
single edge graph E ∗ F (Kn) + δn 2 < E ′ F (Kn). Thus, the (trivial) case in which F contains a
single edge is the only one for which computing E ∗ F (G) is guaranteed to approximate E′ F (G)
within ɛn 2 for any ɛ > 0. In fact, in this degenerate case we actually have E ∗ F (G) = E′ F (G).
References
[1] N. Alon, Ranking tournaments, SIAM J. Discrete Math. 20 (2006), 137-142.
[2] N. Alon, R. A. Duke, H. Lefmann, V. Rödl and R. Yuster, The algorithmic aspects of the
Regularity Lemma, Proc. 33 rd IEEE FOCS, Pittsburgh, IEEE (1992), 473-481. Also: J. of
Algorithms 16 (1994), 80-109.
[3] N. Alon, W. Fernandez de la Vega, R. Kannan and M. Karpinski, Random Sampling and
Approximation of MAX-CSP Problems, Proc. of 34 th ACM STOC, ACM Press (2002), 232-239.
[4] N. Alon, E. Fischer, M. Krivelevich and M. Szegedy, Efficient testing of large graphs, Proc. of
40 th FOCS, New York, NY, IEEE (1999), 656–666. Also: Combinatorica 20 (2000), 451-476.
[5] N. Alon and A. Shapira, A separation theorem in property-testing, manuscript.
[6] N. Alon and A. Shapira, A characterization of the (natural) graph properties testable with
one-sided error, Proc. of FOCS 2005, 429-438.
[7] N. Alon and A. Shapira, Every monotone graph property is testable, Proc. of 37 th STOC 2005,
128-137. Also, SIAM J. on Computing, to appear.
[8] N. Alon and J. H. Spencer, The Probabilistic Method, Second Edition, Wiley, New York,
2000
[9] B. Andrásfai, P. Erdős and V. Sós, On the connection between chromatic number, maximal
clique and minimal degree of a graph, Discrete Math. 8 (1974), 205-218.
[10] S. Arora, A. Frieze and H. Kaplan, A new rounding procedure for the assignment problem with
applications to dense graph arrangement problems, Proc. of 36 th FOCS (1996), 21-30. Also,
Mathematical Programming 92:1 (2002), 1-36.
[11] S. Arora, D. Karger and M. Karpinski, Polynomial time approximation schemes for dense instances
of graph problems, Proc. of 28 th STOC (1995). Also, JCSS 58 (1999), 193-210.
[12] T. Asano, An application of duality to edge-deletion problems, SIAM J. on Computing, 16
(1987), 312-331.
[13] T. Asano and T. Hirata, Edge-deletion and edge-contraction problems, Proc. of STOC (1982),
245-254.
[14] J. Bondy, J. Shen, S. Thomassé and C. Thomassen, Density conditions for triangles in multipartite
graphs, Combinatorica, to appear.
[15] J. Balogh, P. Keevash and B. Sudakov, On the minimal degree implying equality of the largest
triangle-free and bipartite subgraphs, submitted.
31
[16] L. Cai, Fixed-parameter tractability of graph modification problems for hereditary properties,
Information Processing Letters, 58 (1996), 171-176.
[17] K. Cirino, S. Muthukrishnan, N. Narayanaswamy and H. Ramesh, graph editing to bipartite
interval graphs: exact and asymptotic bounds, Proc. of 17 th FSTTCS (1997), 37-53.
[18] E. S. El-Mallah and C. J. Colbourn, The complexity of some edge-deletion problems, IEEE
transactions on circuits and systems, 35 (1988), 354-362.
[19] P. Erdős, On extremal problems of graphs and generalized graphs, Israel J. Math. 2 (1964),
183–190.
[20] P. Erdős, On some new inequalities concerning extremal properties of graphs, Theory of Graphs
(Proc. Colloq., Tihany, 1966), Academic Press, New York, 1968, 77–81.
[21] P. Erdős and M. Simonovits, On a valence problem in extremal graph theory, Discrete Math. 5
(1973), 323-334.
[22] W. Fernandez de la Vega, Max-Cut has a randomized approximation scheme in dense graphs,
Random Structures and Algorithms, 8(3) 1996, 187-198.
[23] E. Fischer, The art of uninformed decisions: A primer to property testing, The Computational
Complexity Column of The Bulletin of the European Association for Theoretical Computer
Science 75 (2001), 97-126.
[24] E. Fischer and I. Newman, Testing versus estimation of graph properties, Proc. of 37 th STOC
2005, 138-146.
[25] A. Frieze and R. Kannan, The regularity lemma and approximation schemes for dense problems,
Proc. of 37 th FOCS, 1996, 12-20.
[26] A. Frieze and R. Kannan, Quick approximation to matrices and applications, Combinatorica,
19(2), 1999, 175-220.
[27] Z. Füredi, Turán type problems, in: Surveys in combinatorics, London Math. Soc. Lecture Note
Ser. 166, Cambridge Univ. Press, Cambridge, 1991, 253–300
[28] M.R. Garey and D.S. Johnson, Computers and Intractability: A guide to the Theory of NP -
Completeness, W.H. Freeman and Co., San Francisco, 1979.
[29] P. W. Goldberg, M. C. Golumbic, H. Kaplan and R. Shamir, Four strikes against physical
mapping of DNA, Journal of Computational Biology 2 (1995), 139–152.
[30] O. Goldreich, Combinatorial property testing - a survey, In: Randomization Methods in Algorithm
Design (P. Pardalos, S. Rajasekaran and J. Rolim eds.), AMS-DIMACS (1998), 45-60.
[31] M. C. Golumbic, H. Kaplan and R. Shamir, On the complexity of DNA physical mapping,
Advances in Applied Mathematics, 15 (1994), 251-261.
[32] P. E. Haxell and V. Rödl, Integer and fractional packings in dense graphs, Combinatorica 21
(2001), 13-38.
32
[33] S. Khot and V. Raman, Parameterized complexity of finding subgraphs with hereditary properties,
COCOON 2000, 137-147.
[34] Y. Kohayakawa, V. Rödl and L. Thoma, An optimal algorithm for checking regularity, SIAM
J. on Computing 32 (2003), no. 5, 1210-1235.
[35] J. Komlós and M. Simonovits, Szemerédi’s Regularity Lemma and its applications in graph
theory. In: Combinatorics, Paul Erdös is Eighty, Vol II (D. Miklós, V. T. Sós, T. Szönyi eds.),
János Bolyai Math. Soc., Budapest (1996), 295–352.
[36] T. Kövari, V.T. Sós and P. Turán, On a problem of K. Zarankiewicz,Colloquium Math. 3 (1954),
50-57.
[37] M. Krivelevich and B. Sudakov, Pseudo-random graphs, to appear.
[38] J. Lewis and M. Yannakakis, The node deletion problem for hereditary properties is NP -
complete, JCSS 20 (1980), 219-230.
[39] A. Natanzon, R. Shamir and R. Sharan, Complexity classification of some edge modification
problems, Discrete Applied Mathematics 113 (2001), 109–128.
[40] M. Parnas, D. Ron and R. Rubinfeld, Tolerant property testing and distance approximation,
manuscript, 2004.
[41] D. Ron, Property testing, in: P. M. Pardalos, S. Rajasekaran, J. Reif and J. D. P. Rolim, editors,
Handbook of Randomized Computing, Vol. II, Kluwer Academic Publishers, 2001, 597–649.
[42] J. D. Rose, A graph-theoretic study of the numerical solution of sparse positive-definite systems
of linear equations, Graph Theory and Computing, R.C. Reed, ed., Academic Press, N.Y., 1972,
183-217.
[43] M. Simonovits, A method for solving extremal problems in graph theory, stability problems,
Theory of Graphs (Proc. Colloq., Tihany, 1966), Academic Press, New York, 1968, 279–319.
[44] E. Szemerédi, Regular partitions of graphs, In: Proc. Colloque Inter. CNRS (J. C. Bermond,
J. C. Fournier, M. Las Vergnas and D. Sotteau, eds.), 1978, 399–401.
[45] D. B. West, Introduction to Graph Theory, Prentice Hall, 2001.
[46] M. Yannakakis, Edge-deletion problems, SIAM J. Comput. 10 (1981), 297-309.
[47] R. Yuster, Integer and fractional packing of families of graphs, Random Structures and Algorithms
26 (2005), 110-118.
[48] J. Xue, Edge-maximal triangulated subgraphs and heuristics for the maximum clique problem.
Networks 24 (1994), 109-120
33
