Sublinear Algorithms for MAXCUT and Correlation Clustering

∗

Aditya Bhaskara1 , Samira Daruki1,2 , and Suresh Venkatasubramanian1

arXiv:1802.06992v1 [] 20 Feb 2018

1

School of Computing, University of Utah
2
Expedia Research

Abstract
We study sublinear algorithms for two fundamental graph problems, MAXCUT and correlation clustering. Our focus is on constructing core-sets as well as developing streaming algorithms
for these problems. Constant space algorithms are known for dense graphs for these problems,
while Ω(n) lower bounds exist (in the streaming setting) for sparse graphs.
Our goal in this paper is to bridge the gap between these extremes. Our first result is to
e 1−δ ) for both the problems, on graphs with average degree nδ (for
construct core-sets of size O(n
any δ > 0). This turns out to be optimal, under the exponential time hypothesis (ETH). Our
core-set analysis is based on studying random-induced sub-problems of optimization problems.
To the best of our knowledge, all the known results in our parameter range rely crucially on
near-regularity assumptions. We avoid these by using a biased sampling approach, which we
analyze using recent results on concentration of quadratic functions. We then show that our
construction yields a 2-pass streaming (1 + ε)-approximation for both problems; the algorithm
e 1−δ ) space, for graphs of average degree nδ .
uses O(n

1

Introduction

Sublinear algorithms are a powerful tool for dealing with large data problems. The range of
questions that can be answered accurately using sublinear (or even polylogarithmic) space or time
is enormous, and the underlying techniques of sketching, streaming, sampling and core-sets have
been proven to be a rich toolkit.
When dealing with large graphs, the sublinear paradigm has yielded many powerful results. For
many NP-hard problems on graphs, classic results from property testing [22, 7] imply extremely
efficient sublinear approximations. In the case of dense graphs, these results (and indeed older ones
of [10, 18]) provide constant time/space algorithms. More recently, graph sketching techniques
have been used to obtain efficient approximation algorithms for cut problems on graphs [2, 3] in a
streaming setting. These algorithms use space that is nearly linear in n (the number of vertices)
and are sublinear in the number of edges as long as |E| = ω(n) (this is called the “semi-streaming”
setting).
By way of lower bounds, recent results have improved our understanding of the limits of sketching and streaming. In a sequence of results [24, 25, 27], it was shown that for problems like matching
and MaxCut in a streaming setting, Ω(n) space is necessary in order to obtain any approximation
better than a factor 2 in one round. (Note that a factor 2 is trivial by simply counting edges.)
∗

This research was supported in part by National Science Foundation under grants IIS-1251049, IIS-1633724

1

Furthermore, Andoni et al. [9] showed that any sketch for all the cuts in a graph must have size
Ω(n).
While these lower bounds show that O(n) space is the best possible for approximating problems
like MaxCut in general, the constructions used in these bounds are quite specialized. In particular,
the graphs involved are sparse, i.e., have Θ(n) edges. Meanwhile, as we mentioned above, if a graph
is dense (Ω(n2 ) edges), random sampling is known to give O(1) space and time algorithms. The
question we study in this paper is if there is a middle ground: can we get truly sublinear (i.e., o(n))
algorithms for natural graph problems in between (easy) dense graphs and (hard) sparse graphs?
Our main contribution is to answer this in the affirmative. As long as a graph has average
degree nδ for some δ > 0, truly sub-linear space (1 + ǫ) approximation algorithms are possible for
problems such as MaxCut and correlation clustering. Note that we consider the max-agreement
version of correlation clustering (see Section 2) Indeed, we show that a biased sample of vertices
forms a “core-set” for these problems. A core-set for an optimization problem (see [1]), is a subset
of the input with the property that a solution to the subset provides an approximation to the
solution on the entire input.
Our arguments rely on understanding the following fundamental question: given a graph G, is
the induced subgraph on a random subset of vertices a core-set for problems such as MaxCut?
This question of sub-sampling and its effect on the value of an optimization problem is well studied.
Results from property testing imply that a uniformly random sample of constant size suffices for
many problems on dense graphs. [18, 6] generalized these results to the case of arbitrary k-CSPs.
More recently, [12], extending a result in [16], studied the setting closest to ours. For graphs, their
results imply that when the maximum and minimum degrees are both Θ(nδ ), then a random induced
e 1−δ ) acts as a core-set for problems such as MaxCut. Moreover, they showed
subgraph with O(n
that for certain lifted relaxations, subsampling does not preserve the value of the objective. Finally,
using more modern techniques, [33] showed that the cut norm of a matrix (a quantity related to
the MaxCut) is preserved up to a constant under random sampling, improving on [18, 6]. While
powerful, we will see that these results are not general enough for our setting. Thus we propose
a new, conceptually simple technique to analyze sub-sampling, and present it in the context of
MaxCut and correlation clustering.

1.1

Our Results

e 1−δ ) for MaxCut
As outlined above, our main result is to show that there exist core-sets of size O(n
1+δ
and correlation clustering for graphs with Ω(n ) edges (where 0 < δ ≤ 1). This then leads to
a two-pass streaming algorithm for MaxCut and correlation clustering on such graphs, that uses
e 1−δ ) space and produces a 1 + ε approximation.
O(n
This dependence of the core-set size on δ is optimal up to logarithmic factors, by a result of [17].
Specifically, [17] showed that any (1+ε) approximation algorithm for MaxCut on graphs of average
1−δ
degree nδ must have running time 2Ω(n ) , assuming the exponential time hypothesis (ETH). Since
a core-set of size o(n1−δ ) would trivially allow such an algorithm (we can perform exhaustive search
over the core-set), our construction is optimal up to a logarithmic factor, assuming ETH.
Our streaming algorithm for correlation clustering can be viewed as improving the semi-streaming
(space Õ(n)) result of Ahn et al. [4], while using an additional pass over the data. Also, in the
context of the lower bound of Andoni et al. [9], our result for MaxCut can be interpreted as saying
that while a sketch that approximately maintains all cuts in a graph requires an Ω(n) size, one
that preserves the MaxCut can be significantly smaller, when the graph has a polynomial average
degree.

2

At a technical level, we analyze the effect of sampling on the value of the MaxCut and correlation clustering objectives. As outlined above, several techniques are known for such an analysis,
but we give a new and conceptually simple framework that (a) allows one to analyze non-uniform
sampling for the first time, and (b) gets over the assumptions of near-regularity (crucial for [16, 12])
and density (as in [18, 6]). We expect the ideas from our analysis to be applicable to other settings
as well, especially ones for which the ‘linearization’ framework of [10] is applicable.
The formal statement of results, an outline of our techniques and a comparison with earlier
works are presented in Section 4.

1.2

Related Work

MaxCut and correlation clustering are both extremely well-studied problems, and thus we will
only mention the results most relevant to our work.
Dense graphs. A graph is said to be dense if its average degree is Ω(n). Starting with the
work of Arora et al. [10], many NP hard optimization problems have been shown to admit a PTAS
when the instances are dense. Indeed, a small random induced subgraph is known to be a core-set
for problems such as MaxCut, and indeed all k-CSPs [22, 6, 18, 31]. The work of [10] relies on an
elegant linearization procedure, while [18, 6] give a different (and more unified) approach based on
“cut approximations” of a natural tensor associated with a CSP.
Polynomial density. The focus of our work is on graphs that are in between sparse (constant
average degree) and dense graphs. These are graphs whose density (i.e., average degree) is nδ , for
some 0 < δ < 1. Fotakis et al. [17] extended the approach of [10] to this setting, and obtained
e 1−δ )). They also showed that it was the
(1 + ε) approximation algorithms with run-time exp(O(n
best possible, under the exponential time hypothesis (ETH). By way of core-sets, in their celebrated
work on the optimality of the Goemans-Williamson rounding, Feige and Schechtman [16] showed
e 1−δ ) is a core-set for MaxCut, if the graphs are almost regular and
that a random sample of O(n
δ
have an average degree n . This was extended to other CSPs by [12]. These arguments seem to use
near-regularity in a crucial way, and are based on restricting the number of possible ‘candidates’
for the maximum cut.
Streaming algorithms and lower bounds.
In the streaming setting, there are several
algorithms [2, 29, 20, 3, 21, 28] that produce cut or spectral sparsifiers with O( ǫn2 ) edges using
Õ( ǫn2 ) space. Such algorithms preserves every cut within (1 + ǫ)-factor (and therefore also preserve
the max cut). Andoni et al. [9] showed that such a space complexity is essential; in fact, [9] show
that any sketch for all the cuts in a graph must have bit complexity Ω( ǫn2 ) (not necessarily streaming
ones). However, this does not rule out the possibility of being able to find a maximum cut in much
smaller space.
For MaxCut, Kapralov et al. [26] and independently Kogan et al. [30] proved that any
streaming algorithm that can approximate the MaxCut value to a factor better than 2 requires
√
Õ( n) space, even if the edges are presented in random order. For adversarial orders, they showed
that for any ǫ > 0, a one-pass (1 + ǫ)-approximation to the max cut value must use n1−O(ǫ) space.
Very recently, Kapralov et al. [27] went further, showing that there exists an ǫ∗ > 0 such that every
randomized single-pass streaming algorithm that yields a (1 + ǫ∗ )-approximation to the MAXCUT
size must use Ω(n) space.
Correlation clustering. Correlation clustering was formulated by Bansal et al. [11] and has
been studied extensively. There are two common variants of the problem – maximizing agreement
and minimizing disagreement. While these are equivalent for exact optimization (their sum is a
constant), they look very different under an approximation lens. Maximizing agreement typically
3

admits constant factor approximations, but minimizing disagreement is much harder. In this paper,
we focus on the maximum-agreement variant of correlation clustering and in particular we focus
on (1 + ǫ)-approximations. Here, Ailon and Karnin [5] presented an approximation scheme with
sublinear query complexity (which also yields a semi-streaming algorithm) for dense instances of
correlation clustering. Giotis and Guruswami [19] described a sampling based algorithm combined
with a greedy strategy which guarantees a solution within (ǫn2 ) additive error. (Their work is
similar to the technique of Mathieu and Schudy [31].) Most recently, Ahn et al. [4] gave a singlepass semi-streaming algorithm for max-agreement. For bounded weights, they provide an (1 + ǫ)approximation streaming algorithm and for graphs with arbitrary weights, they present a 0.766(1 −
ǫ)-approximation algorithm. Both algorithms require (nǫ−2 ) space. The key idea in their approach
was to adapt multiplicative-weight-update methods for solving the natural SDPs for correlation
clustering in a streaming setting using linear sketching techniques.

2

Definitions

Definition 2.1 (MaxCut). Let G = (V, E, w) be a graph with weights w : E → R+ . Let (A, B)
be a partition of V and let w(A, B) denote the sum of weights of edges between A and B. Then
MaxCut(G) = max(A,B) partition of V w(A, B).
For ease of exposition, we will assume that the input graph for MaxCut is unweighted. Our
techniques
apply as long as all the weights are O(1). Also, we denote by ∆ the average degree, i.e.,
P
w
/|V
|.
i,j ij
Moving now to correlation clustering, let G = (V, E, c+ , c− ) be a graph with edge weights c+
ij
+ −
and c−
where
for
every
edge
ij
we
have
c
,
c
≥
0
and
only
one
of
them
is
nonzero.
For
every
edge
ij
ij ij
P
−
ij ∈ E, we define ηij = c+
i∈Γ(j) |ηij |. We will also assume that all
ij − cij and for each vertex, di =
the weights are bounded by an absolute constant in magnitude (for simplicity, we assume it is 1).
We define the “average
degree” ∆ (used in the statements that follow) of a correlation clustering
P
instance to be ( i di )/n.

Definition 2.2 (MAX-AGREE correlation clustering). Given G = (V, E, c+ , c− ) as above, consider
a partition of V into clusters C1 , C2 , . . . , Ck , and let χij be an indicator that is 1 if i an P
j are in the
same cluster and 0 otherwise. The MAX-AGREE score of this clustering is given by ij c+
ij χij +
P −
ij cij (1 − χij ). The goal is to find a partition maximizing this score. The maximum value of the
score over all partitions of V will be denoted by CC(G).
P
P
Note that the objective value can be simplified to ij c−
+ ηij χij = C − + ij ηij χij , where C −
ij
P
denotes the sum ij c−
ij .
We will also frequently use concentration bounds, which we state next.

3

Preliminaries

We will frequently appeal to Bernstein’s inequality for concentration of linear forms of random
variables. For completeness, we state it here.
Theorem 3.1 (Bernstein’s inequality[15]). Let the
X1 , · · · , Xn be independent
P random variables P
with |Xi − E[Xi ]| ≤ b for each i ∈ [n]. Let X = i Xi and let σ 2 = i σi2 be the variance of X.
Then, for any t > 0,
Pr[|X − E[X]| > t] ≤ exp(−
4

t2
)
2σ 2 (1 + bt/3σ 2 )

A slightly more non-standard concentration inequality we use is from Boucheron, Massart and
Lugosi [13]. It can be viewed as an exponential version of the classic Efron-Stein lemma.
Theorem 3.2 ([13]). Assume that Y1 , · · · , Yn are random variables, and Y1n is the vector of these
n random variables. Let Z = f (Y1 , · · · , Yn ), where f : χn → R is a measurable function. Define Z (i) = f (Y1 , · · · , Yi−1 , Yi′ , Yi+1 , · · · , Yn ), where Y1 , · · · , Yn′ denote the independent copies of
Y1 , · · · , Yn . Then, for all θ > 0 and λ ∈ (0, 1θ ),
log E[eλ(Z−E[Z]) ] ≤

λL+
λθ
log E[e θ ],
1 − λθ

where L+ is the random variable defined as
n
X
2
(Z − Z (i) ) 1Z>Z (i) |Y1n ].
L+ = E[
i=1

4

Technical overview

We now present an outline of our main ideas. Suppose we have a graph G = (V, E). First, we define
a procedure vertex sample. This takes as input probabilities pi for every vertex, and produces a
random weighted induced subgraph.
Procedure vertex sample ({pi }i∈V ). Sample a set S ′ of vertices by selecting each vertex vi
with probability pi independently. Define H to be the induced subgraph of G on the vertex set S ′ .
For i, j ∈ S ′ , define wij = pi p1j ∆2 .1
Intuitively, the edge weights are chosen so that the total number of edges remains the same,
in expectation. Next, we define the notion of an importance score for vertices. Let di denote the
degree of vertex i.
i ,ǫ∆}
Definition 4.1. The importance score hi of a vertex i is defined as hi = min{1, max{d
}, where
∆2 αǫ

αǫ is an appropriately chosen parameter (for MaxCut, we set it to
clustering, we set it to

ǫ8
C log n ,

ǫ4
C log n ,

and for correlation

where C is an absolute constant).

The main result is now the following:
Theorem 4.2 (Core-set). Let G = (V, E) have an average degree ∆. Suppose we apply vertex
e n ) vertices and
sample with probabilities pi ∈ [hi , 2hi ] to obtain a weighted graph H. Then H has O(
∆
the quantities MaxCut(H) and CC(H) are within a (1 + ǫ) factor of the corresponding quantities
MaxCut(G) and CC(G), w.p. at least 1 − n12 .
While the number of vertices output by the vertex sample procedure is small, we would like a
core-set of small “total size”. This is ensured by the following.
Procedure edge sample (H). Given a weighted graph H with total edge weight W , sample
′ |w
e
′
each edge e ∈ E(H) independently with probability pe := min(1, 8|S
ε2 W ), to obtain a graph H .
′
Now, assign a weight we /pe to the edge e in H .
The procedure samples roughly |S ′ |/ε2 edges, with probability proportional to the edge weights.
The graph is then re-weighted in order to preserve the total edge weight in expectation, yielding:
1

−
In correlation clustering, we have edge weights to start with, so the weight in H will be wij · c+
ij (or cij ).

5

Theorem 4.3 (Sparse core-set). Let G be a graph n vertices and average degree ∆ = nδ . Let H ′
be the graph obtained by first applying vertex sample and then applying edge sample. Then
e 1−δ ).
e n ) = O(n
H ′ is a ǫ-core-set for MaxCut and CC, having size O(
∆
We then show how to implement the above procedures in a streaming setting. This gives:

Theorem 4.4 (Streaming algorithm). Let G be a graph on n vertices and average degree ∆ = nδ ,
whose edges arrive in a streaming fashion in adversarial order. There is a two-pass streaming algoe n ) = O(n
e 1−δ ) for computing a (1+ǫ)-approximation to MaxCut(G)
rithm with space complexity O(
∆
and CC(G).

Of these, Theorem 4.2 is technically the most challenging. Theorem 4.3 follows via standard
edge sampling methods akin to those in [2] (which show that w.h.p., every cut size is preserved). It
is presented in Section 7, for completeness. The streaming algorithm, and a proof of Theorem 4.4,
are presented in Section 8. In the following section, we give an outline of the proof of Theorem 4.2.

4.1

Proof of the sampling result (theorem 4.2): an outline

In this outline we will restrict ourselves to the case of MaxCut as it illustrates our main ideas.
Let G be a graph as in the statement of the theorem, and let H be the output of the procedure
vertex sample.
Showing that MaxCut(H) is at least MaxCut(G) up to an εn∆ additive term is easy. We
simply look at the projection of the maximum cut in G to H (see, for instance, [16]). Thus, the
challenge is to show that a sub-sample cannot have a significantly larger cut, w.h.p. The natural
approach of showing that every cut in G is preserved does not work as 2n cuts is too many for the
purposes of a union bound.
There are two known ways to overcome this. The first approach is the one used in [22, 16]
and [12]. These works essentially show that in a graph of average degree ∆, we need to consider
only roughly 2n/∆ cuts for the union bound. If all the degrees are roughly ∆, then one can show that
all these cuts are indeed preserved, w.h.p.PThere are two limitations of this argument. First, for
non-regular graphs, the variance (roughly i pd2i , where p is the sampling probability) can be large,
and we cannot take a union bound over exp(n/∆) cuts. Second, the argument is combinatorial,
and it seems difficult to generalize this to analyze non-uniform sampling.
The second approach is via cut decompositions, developed in [18, 6]. Here, the adjacency matrix
A is decomposed into poly(1/ε) rank-1 matrices, plus a matrix that has a small cut norm. It turns
out that solving many quadratic optimization problems (including MaxCut) on A is equivalent
(up to an additive εn∆) to solving them over the sum of rank-1 terms (call this A′ ). Now, the
adjacency matrix of H is an induced square sub-matrix of A, and since we care only about A′
(which has a simple structure), [6] could show that MaxCut(H) ≤ MaxCut(G) + εn2 , w.h.p. To
the best of our knowledge, such a result is not known in the “polynomial density” regime (though
the cut decomposition still exists).
Our technique. We consider a new approach. While inspired by ideas from the works above,
it also allows us to reason about non-uniform sampling in the polynomial density regime. Our
starting point is the result of Arora et al. [10], which gives a method to estimate the MaxCut
using a collection of linear programs (which are, in turn, derived using a sample of size n/∆). Now,
by a double sampling trick (which is also used in the approaches above), it turns out that showing
a sharp concentration bound for the value of an induced sub-program of an LP as above, implies
Theorem 4.2. As it goes via a linear programming and not a combinatorial argument, analyzing
non-uniform sampling turns out to be quite direct. Let us now elaborate on this high level plan.
6

Induced sub-programs.
First, we point out that an analysis of induced sub-programs is
also an essential idea in the work of [6]. The main difference is that in their setting, only the
variables are sub-sampled (and the number of constraints remains the same). In our LPs, the
constraints correspond to the vertices, and thus there are fewer constraints in the sampled LP. This
makes it harder to control the value of the objective. At a technical level, while a duality-based
argument using Chernoff bounds for linear forms suffices in the setting of [6], we need the more
recent machinery on concentration of quadratic functions.
We start by discussing the estimation technique of [10].
Estimation with Linear P
Programs. The rough idea is to start with the natural quadratic
program for MaxCut: max (i,j)∈E xi (1 − xj ), subject to xi ∈ {0, 1}.2 This is then “linearized”
using a seed set of vertices sampled from G. We refer to Section 5 for details. For now, Est(G) is
a procedure that takes a graph G and a set of probabilities {γi }i∈V (G) , samples a seed set using γ,
and produces an estimate of MaxCut (G).
Now, suppose we have a graph G and a sample H. We can imagine running Est(G) and
Est(H) to obtain good estimates of the respective MaxCut values. But now suppose that in both
cases, we could use precisely the same seed set. Then, it turns out that the LPs used in Est(H)
would be ‘induced’ sub-programs (in a sense we will detail in Section 6) of those used in Est(H),
and thus proving Theorem 4.2 reduces to showing a sufficiently strong concentration inequality for
sub-programs.
The key step above was the ability to use same seed set in the Est procedures. This can be
formalized as follows.
Double sampling. Consider the following two strategies for sampling a pair of subsets (S, S ′ )
of a universe [n] (here, qv ≤ pv for all v):
• Strategy A: choose S ′ ⊆ [n], by including every v w.p. pv , independently; then for v ∈ S ′ ,
include them in S w.p. qv /pv , independently.
• Strategy B: pick S ⊆ [n], by including every v w.p. qv ; then iterate over [n] once again,
placing v ∈ S ′ with a probability equal to 1 if v ∈ S, and p∗v if v 6∈ S.
Lemma 4.5. Suppose p∗v = pv (1 −
strategies A and B are identical.

qv
pv )/(1 − pv ).

Then the distribution on pairs (S, S ′ ) obtained by

The proof is by a direct calculation, which we state it here.
Proof. Let us examine strategy A. It is clear that the distribution over S is precisely the same as
the one obtained by strategy B, since in both the cases, every v is included in S independently of
the other v, with probability precisely qv . Now, to understand the joint distribution (S, S ′ ), we
need to consider the conditional distribution of S ′ given S. Firstly, note that in both strategies,
S ⊆ S ′ , i.e., Pr[v ∈ S ′ |v ∈ S] = 1. Next, we can write Prstrategy A [v ∈ S ′ |v 6∈ S] as
pv (1 − pqvv )
Prstrategy A [v ∈ S ′ ∧ v 6∈ S]
=
.
Prstrategy A [v 6∈ S]
1 − pv
Noting that PrstrategyB [v ∈ S ′ |v 6∈ S] = p∗v (by definition) concludes the proof.
2
This is a valid formulation, because for every xi 6= xj that is an edge contributes 1 to the objective, and xi = xj
contribute 0.

7

Proof of Theorem 4.2.
To show the theorem, we use pv as in the statement of the theorem,
n
′
and set q to be the uniform distribution qv = 16εlog
2 ∆ . The proof now proceeds as follows. Let S be
a set sampled using the probabilities pv . These form the vertex set of H. Now, the procedure Est
on H (with sampling probabilities qv /pv ) samples the set S (as in strategy A). By the guarantee of
the estimation procedure (Corollary 5.1.2), we have MaxCut(H) ≈ Est(H), w.h.p. Next, consider
the procedure Est on G with sampling probabilities qv . Again, by the guarantee of the estimation
procedure (Corollary 5.1.1), we have MaxCut(G) ≈ Est(G), w.h.p.
Now, we wish to show that Est(G) ≈ Est(H). By the equivalence of the sampling strategies,
we can now take the strategy B view above. This allows us to assume that the Est procedures
use the same S, and that we pick S ′ after picking S. This reduces our goal to one of analyzing the
value of a random induced sub-program of an LP, as mentioned earlier. The details of this step
are technically the most involved, and are presented in Section 6. This completes the proof of the
theorem. (Note that the statement also includes a bound on the number of vertices of H. This
follows immediately from the choice of pv .)

5

Estimation via linear programming

We now present the estimation procedure Est used in our proof. It is an extension of [10] to the
case of weighted graphs and non-uniform sampling probabilities.
Let H = (V, E, w) be a weighted, undirected graph with edge weights wij , and let γ : V →
[0, 1] denote
sampling probabilities. The starting point is the quadratic program for
P
P MaxCut:
max
w
x
(1
−
x
),
subject
to
x
∈
{0,
1}.
The
objective
can
be
re-written
as
j
i
ij∈E ij i
i∈V xi (di −
P
P
j∈Γ(i) wij xj ),Pwhere di is the weighted degree,
j∈Γ(i) wij . The key idea now is to “guess” the
value of ρi := j∈Γ(i) wij xj , by using a seed set of vertices. Given a guess, the idea is to solve the
following linear program, which we denote by LPρ (V ).
X
maximize
xi (di − ρi ) − si − ti
i

subject to ρi − ti ≤

0 ≤ xi ≤ 1,

X

j∈Γ(i)

wij xj ≤ ρi + si

si , ti ≥ 0.

The
P variables are xi , si , ti . Note that if we fix the xi , the optimal si , ti will satisfy si + ti =
|ρi − j∈Γ(i) wij xj |. Also, note that if we have a perfect guess for ρi ’s (coming from the MaxCut),
the objective can be made ≥ MaxCut(H).

Estimation procedure. The procedure Est is the following: first sample a set S ⊆P
V where each
w
i ∈ V is included w.p. γi independently. For every partition (A, S \ A) of S, set ρi = j∈Γ(i)∩A γijj ,
γ
and solve LPρ (V ) (in what follows, we denote this LP by LPA,S\A
(V ), as this makes the partition
and the sampling probabilities clear). Return the maximum of the objective values.
Our result here is a sufficient condition for having Est(H) ≈ MaxCut(H).

Theorem 5.1. Let H be a weighted graph on n vertices, with edge weights wij that add up to W .
Suppose the sampling probabilities γi satisfy the condition
wij ≤

W ε2 γi γj
P
8 log n u γu

for all i, j.

(1)

Then, we have Est(H, γ) ∈ MaxCut(H) ± εW , with probability at least 1 − 1/n2 (where the
probability is over the random choice of S).
8

The proof of the Theorem consists of claims showing the upper and lower bound separately.
Claim 1. The estimate is not too small. I.e., w.h.p. over the choice of S, there exists a cut
(A, S \ A) of S such that LPA,S\A (H) ≥ MaxCut(H) − εW .

Claim 2.
The estimate is not much larger than an optimal cut. Formally, for any feasible
solution to the LP (and indeed any values ρi ), there is a cut in H of value at least the LP objective.
Proof of Claim 1. Let (AH , V \ AH ) be the max cut in the full graph H. Now consider
a sample
P
w
S, and let (A, S \ A) be its projection onto S. For any vertex i, recall that ρi = j∈Γ(i)∩A γijj =
P
wij
j∈Γ(i)∩AH Yj γj , where Yj is the indicator for j ∈ S. Thus
X

E[ρi ] =

γj

j∈Γ(i)∩AH

wij
=
γj

X

wij .

j∈Γ(i)∩AH

We will use Bernstein’s inequality to bound the deviation in ρi from its mean. To this end, note
that the variance can be bounded as
X

Var[ρi ] =

γj (1 − γj )

j∈Γ(i)∩AH

In what follows, let us write di =
wij
γj

ε2

P

j∈Γ(i) wij

2
wij

γj2

2
X (1 − γj )wij
.
γj

≤

and fi =

j∈Γ(i)

W γi
P
.
u γu

Then, for every j, our assumption

on the wij implies that
≤ 8 log n fi . Thus, summing over j, we can bound the variance by
Now, using Bernstein’s inequality (Theorem 3.1),
!
t2
.
Pr[|ρi − E[ρi ]| > t] ≤ exp − ε2 d f
2t ε2 fi
i i
4 log n + 3 8 log n

ε2 di fi
8 log n .

Setting t = ǫ(di + fi ), and simplifying, we have that the probability above is < exp(−4 log n) =
Thus, we can take a union bound over all i ∈ V , and conclude that w.p. ≥ 1 − n13 ,
ρi −

X

j∈Γ(i)∩AH

wij ≤ ε(di + fi )

for all i ∈ V .

(2)
1
.
n4

(3)

For any S that satisfies the above, considerPthe solution x that sets xi = 1 for i ∈ AH and 0
otherwise. We can choose si + ti = |ρi − j∈Γ(i) wij xj | ≤ ε(di + fi ), by the above reasoning
(eq. (3)). Thus the LP objective can be lower bounded as
X
X
X
xi (di − ρi ) − ε(di + fi ) ≥
xi (di −
wij xj ) − 2ε(di + fi ).
i

This is precisely MaxCut(G) − 2ε
of the claim.

i

P

i (di

j∈Γ(i)

+ fi ) ≥ MaxCut(G) − 4εW . This completes the proof

P
ProofP
of Claim
P2. Suppose we have a feasible solution x to the LP, of objective value i xi (di −
ρi ) − i |ρi − j∈Γ(i) wij xj |, and we wish to move to a cut of at least this value. To this end, define
the quadratic form
X
X

wij xj .
Q(x) :=
xi di −
i

j∈Γ(i)

9

The first observation is that for any x ∈ [0, 1]n , and any real numbers ρi , we have
X
X
X
Q(x) ≥
xi (di − ρi ) −
|ρi −
wij xj |.
i

i

j∈Γ(i)



P
P
This is true simply because Q(x)
P = i xi di − ρi + xi ρi − j∈Γ(i) wij xj , and the fact that the
second term is at least −|ρi − j∈Γ(i) wij xj |, as xi ∈ [0, 1].
Next, note that the maximum of the form Q(x) over [0, 1]n has to occur at a boundary point,
since for any fixing of variables other than a given xi , the form reduces to a linear function of
xi , which attains maximum at one of the boundaries. Using this observation repeatedly lets us
conclude that there is a y ∈ {0, 1}n such that Q(y) ≥ Q(x). Since any such y corresponds to a cut,
and Q(y) corresponds to the cut value, the claim follows.3
Finally, to show Theorem 4.2 (as outlined in Section 4.1), we need to apply Theorem 5.1 with
specific values for γ and wij . Here we state two related corollaries to Theorem 5.1 that imply good
estimates for the MaxCut.
n
for all i.
Corollary 5.1.1. Let H in the framework be the original graph G, and let γi = 16εlog
2∆
W
γ
γ
i
j
ε2
Then the condition wij ≤ 8 log n · P γu holds for all i, j, and therefore Est(G, γ) ∈ MaxCut(G) ±
u

ǫW , w.p. ≥ 1 − n−2 .

The proof is immediate (with a slack of 2), as wij = 1, W = n∆, and all γu are equal.
Corollary 5.1.2. Let H be the weighted sampled graph obtained from vertex sample, and let γi =
16 log n 1
−3
ε2 ∆ pi . Then the condition (1) holds w.p. ≥ 1 − n , and therefore Est(H, γ) ∈ MaxCut(H) ±
ǫW w.p. ≥ 1 − n−2 .
Proof. In this case, we have wij =

1
p i p j ∆2 .

Thus, simplifying the condition, we need to show that

1
2W
1
≤
P
pi pj ∆2
pi pj ∆ u∈H

1
pu

.

P
n
, and u∈H p1u = n.
Now, for H sampled via probabilities pi , we have (in expectation) W = ∆
P
n
A straightforward application of Bernstein’s inequality yields that W ≥ 2∆
and u∈H p1u ≤ 2n,
w.p. at least 1 − n−3 . This completes the proof.

6

Random induced linear programs

We will now show that the Est on H has approximately the same value as the estimate on G (with
γ
(G), where γi = qi . To write the
appropriate γ values). First, note that Est(G) is maxA⊆S LPA,S\A
P
LP, we need the constants ρi , defined by the partition (A, S \A) as ρi := j∈Γ(i)∩A q1j . For the graph
H, the estimation procedure uses an identical program, but the sampling probabilities
are now αi :=
P
p w
qi /pi , and the estimates ρ, which we now denote by ρei , are defined by ρei := j∈Γ(i)∩A jqj ij . Also,
P
P
1
by the way we defined wij , ρei = ρi 2 . The degrees are now dei :=
′
′ wij =
2.
j∈Γ(i)∩S

pi ∆

j∈Γ(i)∩S pi pj ∆

The two LPs are shown in Figure 1.
Our aim in this section is to show the following:

3
We note that the proof in [10] used randomized rounding to conclude this claim, but this argument is simpler;
also, later papers such as [17] used such arguments for derandomization.

10

max

X
[xi (di − ρi ) − (si + ti )]
i∈G

s.t.

X

j∈Γ(i)

−

X

j∈Γ(i)

X

max

i∈S ′

xj ≤ ρi + si , ∀i ∈ [n]

X

s.t.

j∈Γ(i)∩S ′

−

xj ≤ −ρi + ti , ∀i ∈ [n]

X

j∈Γ(i)∩S ′

[xi (dei − ρei ) − (s̃i +t̃i )]

wij xj ≤ ρei + s̃i , ∀i ∈ S ′

wij xj ≤ −e
ρi + t̃i , ∀i ∈ S ′

0 ≤ xi ≤ 1,

0 ≤ xi ≤ 1 ∀i ∈ [n]

s̃i , t̃i ≥ 0 ∀i ∈ S ′

(b) The sampled LP

(a) The LP on the full graph

Figure 1: The two LPs.
Theorem 6.1. Let G be an input graph, and let (S, S ′ ) be sampled as described in Section 4.1.
Then, with probability ≥ 1 − n12 , we have
γ
α
max LPA,S\A
(G) ≥ ∆2 · max LPA,S\A
(H) − εn∆.
A⊆S

A⊆S

Proof outline. To prove the theorem, the idea is to take the “strategy B” viewpoint of sampling
(S, S ′ ), i.e., fix S, and sample S ′ using the probabilities p∗ . Then, we only need to understand
the behavior of an “induced sub-program” sampled with the probabilities p∗ . This is done by
considering the duals of the LPs, and constructing a feasible solution to the induced dual whose
cost is not much larger than the dual of the full program, w.h.p. This implies the result, by linear
programming duality.
γ
Let us thus start by understanding the dual of LPA,S\A
(G) given A, shown in Figure 2a. We
P
note that for any given z, the optimal choice of ui is max{0, di − ρi − j∈Γ(i) zj }; thus we can think
of the dual solution as being the vector z. The optimal ui may thus be bounded by 2di , a fact that
α
we will use later. Next, we write down the dual of the induced program, LPA,S\A
(H), as shown in
Figure 2b.
X
X
minimize
[ũi + ρei z̃i ] s.t.
minimize
ui + ρi zi s.t.
′
i∈S
i∈G
X
X
ũi +
wij z̃j ≥ dei − ρei ∀i ∈ S ′
ui +
zj ≥ di − ρi ∀i ∈ V
j∈Γ(i)∩S ′

j∈Γ(i)

ui ≥ 0,

−1 ≤ zi ≤ 1

ũi ≥ 0,

∀i ∈ V

−1 ≤ z̃i ≤ 1 ∀i ∈ S ′ .

(b) The dual of the induced program
α
(H).
LPA,S\A

γ
(a) The dual of LPA,S\A
(G)

Figure 2: The dual LPs
Following the outline above, we will construct a feasible solution to LP (2b), whose cost is close
to the optimal dual solution to LP (2a). The construction we consider is very simple: if z is the
optimal dual solution to (2a), we set z̃i = zi for i ∈ S ′ as the candidate solution to (2b). This is
clearly feasible, and thus we only need to compare the solution costs. The dual objective values

11

are as follows
DualG =

X
i∈V

DualH ≤

X

i∈S ′

ρi zi + max{0, di − ρi −

X

j∈Γ(i)

zj }

X

ρei zi + max{0, dei − ρei −

j∈Γ(i)∩S ′

(4)
wij zj }

(5)

Note that there is a ≤ in (5), as z̃i = zi is simply one feasible solution to the dual (which is a
minimization program). Next, our goal is to prove that w.p. at least 1 − n12 ,
max DualH ≤
A⊆S

1
εn
· max DualG +
.
2
∆ A⊆S
∆

Note that here, the probability is over the choice of S ′ given S (as we are taking view-B of the
sampling). The first step in proving the above is to move to a slight variant of the quantity DualH ,
which is motivated by the fact that Pr[Yi = 1] is not quite pi , but p∗i (as we have conditioned
on S).
P
1
∗ :=
∗ :=
∗
.
So
also,
let
d
Let us define ρe∗i := p∗ρ∆i 2 (recall that ρei is piρ∆i 2 ), and wij
i
j∈Γ(i) Yj wij .
p ∗ p ∗ ∆2
i

i j

Then, define

Dual∗H :=

X

i∈S ′

ρe∗i zi + max{0, de∗i − ρe∗i −

X

j∈Γ(i)∩S ′

∗
wij
zj }.

(6)

A straightforward lemma, which we use here, is the following. Here we bound the difference
between the “corrected” dual we used to analyze, and the value we need for the main theorem.
εn
.
Specifically, we bound |DualH − Dual∗H | ≤ 2∆
Lemma 6.2. Let (S, S ′ ) be sampled as in Section 4.1. Then w.p. at least 1 −
εn
all z ∈ [−1, 1]n and for all partitions (A, S \ A) of S,4 |DualH − Dual∗H | ≤ 2∆
.
Proof. To prove the lemma, it suffices to prove that w.p. ≥ 1 −
X
i

Yi |e
ρi − ρe∗i | +

X
i

X

Yi

j∈Γ(i)

1
n4 ,

we have that for

1
,
n4

∗
Yj |wij − wij
|≤

εn
.
2∆

(7)

This is simply by using the fact that zi are always in [−1, 1]. Before showing this, we introduce
some notation and make some simple observations. First, denote by Y the indicator vector for S ′
and by X the indicator for S.
Observation 6.3. With probability ≥ 1 −
1. For all i ∈ V ,
2. For all i ∈ V ,
3.
4.
4

P

P

i

Xi (di +ε∆)
pi

i6∈S

P

P

Xj
j∈Γ(i) qj

1
n4

over the choice of (S, S ′ ), we have:

≤ 2(di + ε∆).

Yj
j∈Γ(i),j6∈S p∗j

≤ 2(di + ε∆).

≤ 2ε2 n∆.

Yi (di +ε∆)
p∗i

≤ 2n∆.

Note that the partition defines the ρi .

12

All the inequalities are simple consequences of Bernstein’s inequality (and our choice of parameters pi , p∗i , qi ), and we thus skip the proofs. Next, note that as an immediate consequence of part-1,
we have
ρi ≤ 2(di + ε∆),
for all partitions (A, S \ A) of S.
(8)
Also, note that from the definitions of the quantities (and the fact qi /pi ≤ ε2 ), we have
∀i 6∈ S,

1
ε2
1
− ∗ ≤ ∗
pi pi
pi

(9)

Now, we are ready to show (7). The first term can be bounded as follows:
X
i

Yi |e
ρi − ρe∗i | =

X Y i ρi 1
X ρi 1
X Y i ρi 1
1
1
−
−
1
−
=
.
+
∗
∆2 pi pi
∆2 pi
∆2 pi p∗i
i

i∈S

(10)

i6∈S

Using (8) and part-3 of the observation, the first term can be bounded by O(ε2 n/∆). For the
second term, using (9) together with part-4 of the observation gives a bound of O(ε2 n/∆). Thus
εn
, as we may assume ε is small enough.
the RHS above is at most 16∆
∗ | being “small”.
Now, consider the second term in (7). When i 6∈ S and j 6∈ S, we have |wij − wij
∗ , using
We can easily bound by 2ε2 wij
1
1
1
ε2
2ε2
1
1
1
ε2
+ ∗ − ∗ ∗ ≤ ∗ + ∗ ∗ ≤ ∗ ∗.
− ∗ ∗ ≤
− ∗
pi pj
pi pj
pi pj
pi pj
pi pj
pi pj
pi pj
pi pj
pi pj
In the last steps, we used (9) and the fact that p∗i ≤ pi for i 6∈ S.
∗ | by 2w . Thus we can bound the second
For i ∈ S or j ∈ S, we can simply bound |wij − wij
ij
term in (7) as
X ε2 Yj
X 1 X Yj X Yi
+
.
4
pi ∆2
pj
p∗i ∆2
pj
i∈S

j∈Γ(i)

i6∈S

j∈Γ(i)\S

The second term has only a sum over j not in S – this is why have an extra
P 2 factor for the
P first term.
Y
Now, consider the first term. The inner summation can be written as j∈Γ(i)∩S p1j + j∈Γ(i)\S pjj .
Using parts 1 and 2 of the observation, together with pj ≥ qj /αε , and pj ≥ p∗j for j 6∈ S, we have
P
Yj
j∈Γ(i) pj ≤ 4αε (di + ε∆). Then, using part-3 gives the desired bound on the first term.
Let us thus consider the second term. Again using part 2 along with pj ≥ p∗j for j 6∈ S, we can
bound the inner sum by 2ε2 (di + ε∆). Then we can appeal to part-4 of the observation to obtain
the final claim.
This ends up bounding the second term of (7), thus completing the proof of the lemma.
Thus our goal is to show the following:
Lemma 6.4. Let S satisfy the conditions (a) |S| ≤

2(di + ε∆). Then, w.p. ≥ 1 −

1
n4

over the choice of

max Dual∗H ≤
A⊆S

20n log n
ε2 ∆ ,
′
S given

and (b) for all i ∈ V ,

S, we have

εn
1
.
· max DualG +
∆2 A⊆S
2∆

The condition (b) on S is a technical one that lets us bound ρi in the proofs.
Given Lemma 6.2 and 6.4, it is easy to prove the Theorem 6.1 as follows:
13

P

1
j∈Γ(i)∩S qj

≤

Proof of Theorem 6.1. The conditions we assumed on S in Lemma 6.4 hold w.p. at least 1 − n14
(via a simple application of Bernstein’s inequality). Thus the conclusion of the lemma holds w.p.
at least 1 − n24 . Combining this with Lemma 6.2, we have that maxA DualH ≤ ∆12 maxA DualG + εn
∆
w.p. at least 1 − n34 . The theorem then follows via LP duality.
It thus suffices to prove Lemma 6.4. The main step is to show a concentration bound on a
quadratic function that is not quite a quadratic form. This turns out to be quite technical, and we
discuss it in the following sections.

6.1

Proof of Lemma 6.4

Let Yi = 1i∈S ′ . For convenience, let us denote the max{} terms in equations (4) and (6) by ui and
ũ∗i , respectively. Now,
 

X
1
1
1
Dual∗H − 2 DualG =
(11)
Yi ρe∗i zi − 2 · ρi zi + Yi ũ∗i − 2 · ui .
∆
∆
∆
i

We view the RHS as two summations (shown by the parentheses), and bound them separately.
The first is relatively easy. Recall that by definition, ρe∗i = p∗ρ∆i 2 . Thus the first term is equal to
i

P ρi zi
∗
i p∗i ∆2 Yi − pi . The expectation of this quantity is 0. We will apply Bernstein’s inequality to
bound its magnitude. For this, note that the variance is at most (using |zi | ≤ 1)
X
i

X 4(di + ε∆)2 (1 − p∗ )
ρ2i
∗
∗
i
p
(1
−
p
)
≤
.
i
(p∗i )2 ∆4 i
p∗i ∆4
i

The condition on S gives the bound on ρi that was used above. Next, we note that unless p∗i = 1,
P 4αε ·(di +ε∆)
we have p∗i ≥ (dαi +ε∆)
≤ 8α∆ε n . Next,
2 . Thus the variance is bounded by
i
∆2
ε∆
max
i

2(di + ε∆)
ρi zi
≤
≤ 2αε .
∗
2
pi ∆
p∗i ∆2

(Again, this is because we can ignore terms with p∗i = 1, and for the rest, we have a lower bound.)
Thus, by Bernstein’s inequality,
!
X ρi zi

t2
∗
.
Pr[
Yi − pi ≥ t] ≤ exp − 16αε n
p∗i ∆2
+
2tα
ε
∆
i
2

ε n
Setting t = εn/4∆, the bound simplifies to exp(− C∆α
), for a constant C. Thus, by our choice of
ε
αε and our size bound on |S|, this is < exp(−|S|)/n4 .
The second term of (11) requires most of the work. We start with the trick (which turns out
to be important) of splitting it into two terms by adding a “hybrid” term, as follows:

 X
X
X
ui
1
1
ui
∗
∗
Yi ũi − Yi ∗ 2 +
Yi ũi − 2 · ui =
Yi ∗ 2 − 2 · ui .
∆
pi ∆
pi ∆
∆
i

i

i

The second term will again be bounded using Bernstein’s inequality (in which we use our
earlier observation that ui = O(di )). This gives an upper bound of εn/8∆, with probability
1 − exp(−|S|)/n4 . We omit the easy details.
14

Let us focus on the first term. We now use the simple observation that max{0, A}−max{0, B} ≤
|A − B|, to bound it by
X
i

Yi de∗i − ρe∗i −

X

j∈Γ(i)∩S ′

∗
wij
zj −

1
p∗i ∆2

di − ρi −

X

zj

j∈Γ(i)



.

By the definition of ρe∗i , it cancels out. Now, writing cj = 1 − zj (which now ∈ [0, 2]) and using
the definition of de∗i , we can bound the above by
X

X

Yi

i

j∈Γ(i)

∗
Yj wij
cj −

1

cj
p∗i ∆2

=

X
i

Yi

X

j∈Γ(i)

∗
wij
cj (Yj − p∗j )

∗
(using wij
=

1

)
p∗i p∗j ∆2

Showing a concentration bound for such a quadratic function will be subject of the rest of the
section. Let us define
f (Y ) := f (Y1 , . . . , Yn ) :=

X

Yi

i

X

j∈Γ(i)

∗
wij
cj (Yj − p∗j ) .

(12)

We wish to show that Pr[f > εn
∆ ] ≤ exp(−|S|). Unfortunately, this is not true – there are
counter-examples (in which the neighborhoods of vertices have significant overlaps) for which it is
not possible to obtain a tail bound better than exp(−n/∆2 ), roughly speaking. To remedy this, we
resort to a trick developed in [22, 16]. The key idea is to condition on the event that vertices have
a small weighted degree into the set S ′ , and obtain a stronger tail bound.
“Good” conditioning. We say that a choice of Y ’s is good if for all i ∈ V , we have
X
ε∆ + 2di
∗
.
wij
Yj ≤
p∗i ∆2
j∈Γ(i)

The first lemma is the following.
Lemma 6.5. Let H be the weighted graph on S ′ obtained by our algorithm. For any vertex i ∈ V ,
we have


X
ε∆
+
2d
1
i
∗
wij
Yj >
Pr 
< 6.
∗
2
pi ∆
n
j∈Γ(i)

Proof. Fix some i ∈ V , and consider

P

∗
j∈Γ(i) wij Yj =

1
p∗i ∆2

P

Yj
j∈Γ(i) p∗j



. The term in the paren-

thesis has expectation precisely di . Thus, applying Bernstein using maxj
P
p∗j (1−p∗j )
≤ di maxj p1∗ , we have
j∈Γ(i) (p∗ )2

1
p∗j

j

j

Pr



X

j∈Γ(i)∩VH




Yj
εt2
> di + t ≤ exp −
.
p∗j
(di + t)αε ∆

By setting t = (di + ε∆), the RHS above can be bounded by




1
ε2
ε(di + ε∆)2
< 6.
≤ exp −
exp −
(2di + ε∆)αε ∆
2αε
n
This completes the proof, using our choice of αε .
15

≤

αε ∆
ε ,

together with

Conditioning on the Y being good, we show the following concentration theorem.
Theorem 6.6. Let Yi ’s be independent random variables, that are 1 w.p. p∗i and 0 otherwise, and
let f (Y ) be defined as in (12). Then we have

εn
Pr f (Y ) ≥
8∆


1
2
Y is good ≤ 5 · e−20n log n/ε .
n

We observe that the theorem implies Lemma 6.4. This is because by the Theorem and the
, for any A ⊆ S. Thus
preceeding discussions, Pr[Dual∗H − DualG ≤ εn/∆ | Y good] ≥ 1 − exp(−|S|)
n5
by union bound over A, Pr[maxA Dual∗H − maxA DualG ≤ εn/∆ | Y good] ≥ 1 − n15 . Since the
probability of the good event is at least 1 − n15 (by Lemma 6.5), the desired conclusion follows.

6.2

Concentration bound for quadratic functions

To conclude our proof, it suffices to show Theorem 6.6. To bound the quadratic function f , we
bound the moment generating function (MGF), E[eλf | good]. This is done via a decoupling
argument, a standard tool for dealing with quadratic functions. While decoupling is immediate for
‘standard’ quadratic forms, the proof also works for our f (which has additional absolute values).
The rest of the proof has the following outline.
Proof outline. The main challenge is the computation of the MGF under conditioning (which
introduces dependencies among the Yi , albeit mild ones). The decoupling allows us to partition
vertices into two sets, and only consider edges that go across the sets. We then show that it suffices
to bound the MGF under a “weakened” notion of conditioning (a property we call δ-good). Under
this condition, all the vertices in one of the sets of the partition become independent, thus allowing
a bound on the moment — in terms of quantities that depend on the variables on the other set of
the partition. Finally, we appeal to a strong concentration bound of Boucheron et al. [13] to obtain
an overall bound, completing the proof.
We now expand the proof outline above.
Decoupling. Consider independent Bernoulli random variables δi that take values 0 and 1 w.p.
1/2 each, and consider the function
X
X
∗
fδ :=
δi Yi |
(1 − δj )wij
cj (Yj − p∗j )|
i

j∈Γ(i)

Using the fact that E[|g(x)|] ≥ |E[g(x)]| for any function g, and defining Eδ as the expectation with
respect to the δi ’s, we have
Eδ fδ = Eδ

X
i

≥
=

δi Yi |

X1
2

· Yi |

X1
4

· Yi |

i

i

X

j∈Γ(i)

X

j∈Γ(i)

X

j∈Γ(i)

∗
(1 − δj )wij
cj (Yj − p∗j )| =

X1
i

2

· Yi E δ |

X

j∈Γ(i)

∗
(1 − δj )wij
cj (Yj − p∗j )|

∗
Eδ (1 − δj )wij
cj (Yj − p∗j )|

1
∗
wij
cj (Yj − p∗j )| = f
4

We used the fact that i never appears in the summation term involving Yi to obtain the first
equality. Next, using Jensen’s inequality, we have:
EY [eλf | good] ≤ EY [e4λEδ fδ | good] ≤ EY,δ [e4λfδ | good]
16

where EY,δ means the expectation with respect to both random variables Y and δ. Now, the
interpretation of fδ is simply the following. Consider the partitioning (V + , V − ) of V defined by
V + = {i ∈ [n] : δi = 1} and V − = {i ∈ [n] : δi = 0}, then
X
X
∗
fδ =
Yi
wij
cj (Yj − p∗j ) .
i∈V +

j∈Γ(i)∩V −

P
∗
∗
+
For
Pconvenience, define Ri = | j∈Γ(i)∩V − wij cj (Yj − pj )|, for i ∈ V . Thus we can write
fδ = i∈V + Yi Ri . The condition that Y is good now gives us a bound on Ri . For any cj (it is
important to note that the good condition does not involve the constants cj , as those depend on
the LP solution; all we know is that 0 ≤ cj ≤ 2), we have
X
∗
2wij
(Yj + p∗j )
Ri ≤
j∈Γ(i)∩V −

≤

2di
2ε∆ + 6di
2(ε∆ + 2di )
+ ∗ 2 ≤
.
∗
2
pi ∆
pi ∆
p∗i ∆2

Now, the quantity we wish to bound can be written as
λf

E[e
Y

| good] ≤ E [e4λfδ | good] ≤ E E E [e4λ
Y,δ

δ Y

−

Y

+

P

i∈V +

Yi Ri

| good].

(13)

The key advantage that decoupling gives us is that we can now integrate over Yi ∈ V + , i.e., evaluate
the innermost expectation, for any given choice of {Yi : i ∈ V − } (which define the Ri ). The problem
with doing this in our case is that the good condition introduces dependencies on the Yi , for i ∈ V + .
Fortunately, weakening conditioning does not hurt much in computing expectations. This is
captured by the following simple lemma.
Lemma 6.7. Let Ω be a space with a probability measure µ. Let Q1 and Q2 be any two events such
that Q1 ⊂ Q2 , and let Z : Ω 7→ R+ be a non-negative random variable. Then,
E[Z|Q1 ] ≤

E[Z|Q2 ]
.
Pr[Q1 ]

Proof. Let Ω1 (resp. Ω2 ) be the subset of Ω in which Q1 (resp. Q2 ) is satisfied. By hypothesis,
Ω1 ⊆ Ω2 . Now by the definition of conditional expectation, and the non-negativity of Z, we have
Z
Z
1
1
µ(Q2 )
E[X|Q1 ] =
E[Z|Q2 ].
Z(x)µ(x)dx ≤
Z(x)µ(x)dx =
µ(Q1 ) x∈Ω1
µ(Q1 ) x∈Ω2
µ(Q1 )
Since µ(Q2 ) ≤ 1, the conclusion follows.
Weaker good property.
The next crucial notion we define is a property “δ-good”. Given
a δ ∈ {0, 1}n (and corresponding partition (V + , V − )), a set of random variables Y is said to be
δ-good if for all i ∈ V + , we have
X

j∈Γ(i)∩V −

∗
wij
Yj ≤

ε∆ + 2di
.
p∗i ∆2

(14)

We make two observations. First, the good property implies the δ-good property, for any
choice of δ. Second, and more crucial to our proof, conditioning on δ-good does not introduce any
17

dependencies on the variables {Yi : i ∈ V + }. Now, continuing from (13), and using the fact that
the good condition holds with probability > 1/2, we have
4λ
E E E [e

P

i∈V +

Yi Ri

δ Y−Y+

| good] ≤ E E E [2e4λ

P

i∈V +

Yi Ri

δ Y−Y+

| δ-good].

Now P
for any 0/1 choices for variables Y − , the Ri ’s get fixed for every i ∈ V + , and we can bound
EY + [e4λ i∈V + Yi Ri | Ri ] easily.
Lemma 6.8. Let Yi be independent random 0/1 variables taking value 1 w.p. p∗i , and let Ri be
given, for i ∈ V + . Suppose λ > 0 satisfies |λRi | ≤ 1 for all i. Then
EY + [eλ

P

i∈V +

Yi Ri

P

]≤e

i∈V +

λp∗i Ri +λ2 p∗i R2i

.

Proof. Since the lemma only deals with i ∈ V + , we drop the subscript for the summations and
expectations. One simple fact we use is that for a random variable Z with |Z| ≤ 1,
2

E[eZ ] ≤ E[1 + Z + Z 2 ] ≤ eE[Z]+E[Z ] .
Using this, and the independence of Yi together with Yi2 = Yi ,
P
Y
Y
2 2
eE[λYi Ri ]+E[λ Ri Yi ] .
E[eλ Yi Ri ] =
E[eλYi Ri ] ≤

(15)

As E[Yi ] = p∗i , this completes the proof of the lemma.

Using the lemma, replacing λ with 4λ yields the following
P
 P

∗
2 ∗ 2
4λ i∈V + Yi Ri
| δ-good] ≤ E E e i∈V + 4λpi Ri +16λ pi Ri | δ-good .
E E E [e
δ Y− Y+

δ Y−

(16)

The second term in the summation is already small enough. I.e., using (14)
X
n
λ2 p∗i Ri2 ≤ 2λ2 αε .
∆
+
i∈V

While the bound on Ri can be used to bound the first term, it turns out that this is not good
enough. We thus need a more involved argument. Thus the focus is now to bound
X
 λg(Y )

| δ-good , where g(Y ) :=
p∗i
E E e
δ Y−

i∈V +

X

j∈Γ(i)∩V −

∗
wij
cj (Yj − p∗j ) .

Outline: concentration bound for g. To deduce a concentration bound for g, we first remove
the conditioning (again appealing to Lemma 6.7). This then gives us independence for the Yj , for
j ∈ V − . We can then appeal to the fact that changing a Yj only changes g by a small amount,
to argue concentration. However, the standard “bounded differences” concentration bound ([13],
Theorem ...) will not suffice for our purpose, and we need more sophisticated results [13] (restated
as Theorem 3.2).
To use the same notation as the theorem, define Z = g(Y ), where we only consider Yr , r ∈ V − .
Now, for any such r, consider Z − Z (r) . Since Z (r) is obtained by replacing Yr by an independent
Yr′ and re-computing g, we can see that the only terms i which could possibly be affected are
i ∈ Γ(r) ∩ V + . Further, we can bound the difference |Z − Z (r) | by
X
∗
|Z − Z (r) | ≤ |Yr − Yr′ |
2p∗i wir
,
i∈Γ(r)∩V +

18

where we have used |cr | ≤ 2. The summation can be bounded by
Then, to use the theorem, we need

2dr
.
p∗r ∆2

Denote this quantity by θr .

EYr′ |Z − Z (r) |2 ≤ |Yr − Yr′ |2 θr2 ≤ |Yr − Yr′ |θr2 ≤ Yr θr2 + p∗r θr2 .
We used the fact that |Yr − Yr′ | ∈ {0, 1}. Now applying Theorem 3.2 by setting θ =
EV − [eλ(g−E[g]) ] ≤ EV − [e

λ2
2

P

r∈V −

Yr θr2 +p∗r θr2

1
2λ ,

we have:

]
2Y

Once again, since λθr will turn out to be < 1, we can use the bound E[eλ
conclude that
P
n
2
∗ 2
2
EV − [eλ(g−E[g]) ] ≤ e2λ r∈V − pr θr ≤ e4λ αε ∆ .

2
r θr /2

2 θ 2 p∗
r r

] ≤ eλ

, and

The last inequality is due to a reasoning similar to earlier.
We are nearly done. The only step that remains for proving Theorem 6.6 is to obtain a bound
on E[g]. For this, we need to bound, for any i, the term
E[|Ri |] = E[|

X

j∈Γ(i)∩V −

∗
wij
cj (Yj − p∗j )|].

By Cauchy-Schwartz and the fact that Yj are independent, we have
X
∗ 2
E[|Ri |]2 ≤ E[Ri2 ] =
) cj p∗j (1 − p∗j )
(wij
j∈Γ(i)∩V −

≤

X 1 − p∗j
αε di
1
≤
.
∗
∗
2
4
(pi ) ∆
pj
ε(p∗i )2 ∆3
j∈Γ(i)

We have used the fact that (1 − p∗j )/p∗j ≤ αε ∆/ε. Thus we have
E[

X

i∈V +

p∗i |Ri |] ≤

 α 1/2
 α 1/2 X
n  αε 1/2
1/2
ε
ε
1/2
.
≤
d
n∆
≤
i
ε∆3
ε∆3
∆ ε
i

From our choice of αε , this is < 41 · εn
∆.
Putting everything together, we get that the desired moment
 εn
n
.
+ λ2 αε
≤ exp λ
4∆
∆
To complete the bound, we end up setting λ =

ε
αε .

For this value of λ, we must ensure that

ε (dr + ε∆)
≤ 1,
αε p∗r ∆2
which is indeed true.

7

Sparse Core-set for Max-Cut

In Theorem 4.2, we have shown that there is a core-set (i.e., a smaller weighted graph with the
same MAXCUT value) with a small number of vertices. We now show that the number of edges
can also be made small (roughly n/∆). This will prove Theorem 4.3.
We start with a lemma about sampling edges in graphs with edge weights ≤ 1. (We note that
essentially the same lemma is used in several works on sparsifiers for cuts.)
19

Lemma 7.1. Consider the weighted graph H resulted from vertex sample, defined on set of
vertices S ′ in which wij < 1. If we apply edge sample on H, then the resulted graph H ′ have
MaxCut(H ′ ) ± (1 + ǫ)MaxCut(H)

with probability at least 1 −

1
.
n2

Let us first see that the lemma gives us Theorem 4.3.
Proof of Theorem 4.3. We only need to verify the bound on the number of edges. As every edge
8w
is sampled with probability pij = min(1, ε2ij ), and since the total edge weight is normalized to be
|S ′ |, we have that the expected number of edges is ≤
16|S ′ |
,
ε2

completing the proof.

7.1

Proof of Edge Sampling

8|S ′ |
,
ε2

and w.p. at least 1 −

1
,
n4

this is at most

We now prove Lemma 7.1.
Proof. Recall that in edge sample algorithm we first rescale the edge weights so that they sum
up to |S ′ |, and then sample each edge ij in the resulted graph (also denoted H, as we can assume it
′ = wij to
to be a pre-processing) with probability pij = min(wij ǫ82 , 1) and reweigh the edges to wij
pij
obtain the graph H ′ . Define the indicator variable Xij for each edge eij in graph H, where Xij = 1
if the corresponding edge eij is selected by edge sample and Xij = 0 otherwise.
Our goal is to show that all cuts are preserved, w.h.p. Consider any cut (A, B) in H. Call the
′
set of all the edges
and theP
set of in the cut on the sampled graph H ′ as CA,B
.
P on this cut as CA,B
′
′
′
Set w(CA,B ) = eij ∈CA,B wij and w (CA,B ) = eij ∈C ′ wij . Then we have,
A,B
X
X
X
X
′
′
′
′
E[w′ (CA,B
)] = E[
wij
Xij ] =
wij
Pr(Xij = 1) =
pij wij
=
wij = w(CA,B ).
We will now apply Bernstein’s inequality to bound the deviation. For this, the variance is first
bounded as follows.
2
X
X
X wij
ǫ2
ǫ2 X
′
′ 2
wij = w(CA,B )
(1 − pij ) ≤
Xij ] =
wij
Var[
wij
Var(Xij ) ≤
pij
8
8

We used the inequality that unless pij = 1 (in which case the term drops out), we have wij /pij ≤
ε2 /8.
By the observation on wij /pij above, we can use Bernstein’s inequality 3.1 with b = ε2 /8, to
obtain
!
2
X
t
′
Pr[|
wij
Xij − w(CA,B )| ≥ t] ≤ exp − ε2 w(C )
.
A,B
tε2
+
4
8

Setting t = εW , where W is the sum of all the edge weights (which is equal to |S ′ | after the
pre-processing), the bound above simplifies to exp(−2|S ′ |), and thus we can take a union bound
over all cuts. This completes the proof.

8

A 2-pass streaming algorithm

We now show how our main core set result can be used to design a streaming algorithm for MaxCut.
e
The algorithm works in two passes: the first pass builds a core-set S of size O(n/∆)
as prescribed
by Theorem 4.2 and the second pass builds the induced weighted graph G[S] and computes its max
cut. This algorithm works under edge insertion/deletion.
20

8.1

Pass 1: Building a core set

To construct S, Theorem 4.2 states that each vertex must be sampled with probability pi , where
i ,ǫ∆)
) is the importance score of a vertex. As the goal is to only choose
pi ≥ hi and hi = min(1, max(d
∆2 αǫ
a small number of vertices, we will also make sure that pi ≤ 2hi . The challenge here is two-fold:
we need to sample (roughly) proportional to the degree di , which can only be computed after the
stream has passed, and we also need the actual value of pi (or a close enough estimate of it) in
order to correctly reweight edges in the second pass.
The degree di of a vertex i is the “count” of the number of times i appears in the edge stream.
To sample with probability proportional to di we will therefore make use of streaming algorithms
for ℓ1 -sampling [32, 8, 23]. We borrow some notation from [8].
Definition 8.1. Let ρ > 0, f ∈ [1, 2]. A (ρ, f )-approximator to τ > 0 is a quantity τ̂ such that
τ /f − ρ ≤ τ̂ ≤ f τ + ρ

Lemma 8.2 ([23] (rephrased from [8])). Given a vector x ∈ Rn and parameters ǫ, δ > 0, c > 0
there exists an algorithm A that uses space O(log(1/ǫ)ǫ−1 log2 n log(1/δ)) and generates a pair (i, v)
from a distribution Dx on [1 . . . n] such that with probability 1 − δ
• Dx (i) is a ( n1c , 1 + ǫ)-approximator to |xi |/kxk1

• v is a (0, 1 + ǫ)-approximator to xi

where c is a fixed constant.

We will also need to maintain heavy hitters: all vertices of degree at least ∆2 (up to constants).
To do this, we will make use of the standard CountMin sketch [14]. For completeness, we state
its properties here.
Lemma 8.3 ([14]). Fix parameters k, δ > 0. Then given a stream of m updates to a vector x ∈ Rn
there is a sketch CM of size O(k log δ−1 (log m + log n)) and a reconstruction procedure f : [n] → R
such that with probability 1 − δ, for any xi , |xi − f (i)| ≤ kxk1 /k

Outline. We will have a collection of r, roughly n/∆ ℓ1 -samplers. These samplers will together
give a good estimate ((1 + ε)-approximation) of the importance hi for all the vertices that have a
small degree (which we define to be < αε ∆2 ). Then, we use the CM sketch to maintain the degrees
of all the ‘high degree’ vertices, i.e., those with degrees ≥ αε ∆2 . Taken together, we obtain the
desired sampling in the first pass.
Definition 8.4. Given two sets of pairs of numbers S, S ′ , let S∪max S ′ = {(x, max(x′ ,y)∈S∪S ′ ,x′ =x y)}
Lemma 8.5. Let S = {(i, vi )} be the set returned by Algorithm 1. Then

e n .
• S has size O
∆

• Each i ∈ [n] is sampled with probability pi that is (0, 1 + ǫ) approximated by vi and that
(n−c , 1 + ǫ)-approximates hi .

Proof sketch. Consider any vertex i with di ≥ ∆2 αǫ . By Lemma 8.3, such a vertex will report a
count of at least f (i) = (1 − ζ)∆2 αǫ and thus is guaranteed to be included in Sh . Its reported score
vi = 1 satisfies the requirement of the Lemma. Secondly, consider any vertex with degree di < ǫ∆.
For such a vertex, hi = ǫ/∆αǫ and thus it is included in Sl with the desired probability and vi .
Finally, consider a vertex i with ǫ∆ ≤ di < ∆2 αǫ . The probability that none of the ℓ1 -samplers
yield i is (1 − di /n∆)r , and since di /n∆ ≪ 1, this can be approximated as (1 − rdi /n∆). Thus, the
probability of seeing i is rdi /n∆ = di /∆2 αǫ as desired.
Corollary 8.5.1. For each (i, vi ) ∈ S, hi ≤ vi ≤ 2hi .
21

Algorithm 1 Given n and average degree ∆
Initialize Sl , Sm , Sh ← ∅, and ζ = αε .
Sample elements from [1 . . . n] each with probability ǫ/∆αǫ . For each sampled i add (i, ǫ/∆αǫ )
to Sl .
Fix ζ > 0. Initialize a CountMin sketch CM with size parameter k = n/∆ζ 2 . Let f be the
associated reconstruction procedure.
Initialize r = O(n/∆αǫ ) copies A1 . . . Ar of the algorithm A from Lemma 8.2.
for each stream update (i, w) (a vertex to which current edge is incident, and weight) do
Update each Aj , 1 ≤ j ≤ r.
Update CM.
end for
for j = 1 to r do
Sample (i, v) from Aj . Sm = Sm ∪max {(i, v)}
end for
Sh = {(i, 1) | f (i) ≥ (1 − ζ)∆2 αǫ }
return Sl ∪max Sm ∪max Sh

8.2

Pass 2: Building the induced weighted graph

e
The first pass produces a set S of O(n/∆)
vertices together with estimates vi for their importance
score hi . If we weight each edge ij in G[S] by wij = 1/vi vj ∆2 , Theorem 4.2, along with Corollary 8.5.1 guarantee that a MaxCut in the resulting weighted graph is a good approximation of
the true max cut.
Thus, knowing S, constructing the re-weighted G[S] in the second pass is trivial if we had space
proportional to the number of edges in G[S]. Unfortunately this can be quadratic in |S|, so our
goal is to implement the edge sampling of Theorem 4.3 in the second pass. This is done as follows:
we maintain a set of edges E ′ . Every time we encounter an edge ij with i, j ∈ S, we check to see if
it is already in E ′ . If not, we toss a coin and with probability pij = min(1, wij log n/ǫ2 ) we insert
e
(i, j, wij /pij ) into E ′ . By Lemma 7.1, the size of E ′ is O(n/∆),
and the resulting graph yields a
(1 + ǫ) approximation to the MaxCut.

9

Correlation Clustering

Our argument for correlation clustering parallels the one for MaxCut. The MAX-AGREE variant
of correlation clustering, while not a CSP (as the number of clusters is arbitrary), almost behaves
as one.
We start with two simple observations. The first is that we can restrict the number of clusters
to 1/ε, for the purposes of a (1 + ε) approximation (Lemma 9.1) . Next, observe that the optimum
objective value is at least max{C + , C − } ≥ n∆/2. This is simply because placing all the vertices
in a single cluster gives a value C + , while placing them all in different clusters gives C − . Thus, it
suffices to focus on additive approximation of εn∆.
Lemma 9.1. Let C be the optimal clustering, and let OPT be its max-agree cost. Then there exists
a clustering C ′ that has cost ≥ (1 − O(ε))OP T , and has at most 1/ε clusters.
The lemma is folklore in the correlation clustering literature.
Proof. Let A1 , A2 , . . . , Ak be the clusters in the optimal clustering C. Now, suppose we randomly
color the clusters with t = 1/ε colors, i.e., each cluster Ai is colored with a random color in [t].
22

We then merge all the clusters of a given color into one cluster, thus obtaining the clustering C ′ .
Clearly, C ′ has at most t colors.
Now, we observe that if u, v ∈ Ai to begin with, then u, v are still in the same cluster in C ′ .
But if u ∈ Ai and v ∈ Aj , and the colors of Ai and Aj are the same, then u and v are no longer
separated in C ′ . Let us use this to see what happens to the objective. Let χuv be an indicator for
u, v being in the same cluster in the optimal clustering C, and let χ′uv be a similar indicator in C ′ .
The original objective is
X
C− +
ηij χij .
ij

From the above reasoning, if χuv = 1, then χ′uv = 1. Also, if χuv = 0, E[χ′uv ] = 1/t, i.e., there
is a probability precisely 1/t = ε that the clusters containing u, v now get the same color. Thus,
we can write the expected value of the new objective as
X
X
C− +
ηij χij + ε
ηij (1 − χij ).
ij

P

ij

P

Let us denote S = ij ηij χij and S ′ = ij ηij (1 − χij ). Then by definition, we have S + S ′ =
+
−
ij ηij = C − C . Now, to show that we have a (1 − ε) approximation, we need to show that
C − + S + εS ′ ≥ (1 − ε)(C − + S). This simplifies to C − + S + S ′ ≥ 0, or equivalently, C + ≥ 0,
which is clearly true.
This completes the proof.
P

Once we fix the number of clusters k, we can write correlation clustering as a quadratic program
in a natural way: for each vertex i, have k variables
xiℓ , which is supposed to indicate if i is given
P
the label ℓ. We then have the constraint that ℓ xiℓ = 1 for all i. The objective function then has
a clean form:
k
XX
XX
X
+
[
xiℓ (1 − xjℓ )c−
xiℓ c−
xiℓ (ρiℓ + d−
ij + xiℓ xjℓ cij ] =
ij + xiℓ xjℓ ηij =
i ),
ij

ij

ℓ=1

ℓ

P

i,ℓ

P

−
where xiℓ = 1 iff vertex i ∈ Cℓ , and ρiℓ = j∈Γ(i) xjℓ ηij and d−
i =
j cij .
Note the similarity with the program for MaxCut. We will show that the framework from
Section 4.1 carries over with minor changes. The details of the new Est procedure can be found
in Section 9.1 (it requires one key change: we now need to consider k-partitions of the seed set in
order to find ρ). The duality based proof is slightly more involved; however we can use the same
rough outline. The proof is presented in Section 9.2.

9.1

LP Estimation Procedure for Correlation Clustering

−
Let H = (V, E, c) be a weighted, undirected graph with edge weights c+
ij and cij as before, and let
γ : V → [0, 1] denote sampling probabilities. We define EstC (H, γ) to be the output of the following
randomized algorithm: sample a set S by including each vertex i in it w.p. γi independently; next,
for each partition (A1 , · · · , Ak ) of S, solve the LP defined below, and output the largest objective
value.

23

LPA1 ,··· ,Ak (V ) is the following linear program. (As before, we use constants ρiℓ :=
maximize

X
i

subject to

xiℓ (ρiℓ + d−
i ) − (siℓ + tiℓ )

ρiℓ − tiℓ ≤
X
ℓ

X

j∈Γ(i)

ηij xjℓ ≤ ρiℓ + siℓ

P

ηij
j∈Γ(i)∩Aℓ γj .)

∀i, ℓ

xiℓ = 1 ∀i ∈ [n]

siℓ , tiℓ ≥ 0

∀i, ℓ

P
Once again, the best choice of siℓ , tiℓ for each pair i, ℓ are so that siℓ + tiℓ = |ρiℓ − j∈Γ(i) ηij xjℓ |.
We now show a result analogous to the one earlier – that under appropriate conditions, EstC
is approximately equal to the optimal correlation clustering objective value.
−
Theorem 9.2. Let H be a weighted graph on n vertices with edge weights c+
ij , cij that add up to W .
Suppose the sampling probabilities γi satisfy the condition

wij ≤

γγ
W ε2
Pi j
2
8k log n u γu

for all i, j.

(17)

Then, we have EstC (H, γ) ∈ CC(H) ± εW , with probability at least 1 − 1/n2 (where the probability
is over the random choice of S).
Note that the only difference is the k 2 term in (17).
Proof. As before, the proof follows from two complementary claims.
Claim 1. W.h.p. over the choice of S, there exists a partitioning (A1 , · · · , Ak ) of S such that
LPA1 ,··· ,Ak (H) ≥ CC(H) − εW .
Claim 2. Consider any feasible solution to the LP above (for some values ρiℓ , siℓ , tiℓ ). There exists
a partitioning in H of objective value at least the LP objective.
The proof of Claim 1 mimics the proof in the case of MAXCUT. We use Bernstein’s inequality
for every ℓ ∈ [k] with deviation being bounded by ε(di + ∆)/k in each term. This is why we need
an extra k2 term in the denominator of (17). We omit the details.
P
−
Proof of
PClaim 2.PSuppose we have a feasible solution x to the LP of objective value i,ℓ xiℓ (di +
ρiℓ ) − i,ℓ |ρiℓ − j∈Γ(i) ηij xjℓ |, and we wish to move to a partitioning of at least this value. To
this end, define the quadratic form
X
X

Q(x) :=
ηij xjℓ .
xiℓ d−
i +
i,ℓ

j∈Γ(i)

The first observation is that for any x ∈ [0, 1]nk , and any real numbers ρi , we have
X
X
X
|ρiℓ −
ηij xjℓ |.
Q(x) ≥
xiℓ (d−
i + ρiℓ ) −
i,ℓ

i,ℓ

j∈Γ(i)



P
P
This is true simply because Q(x) =P i,ℓ xiℓ d−
j∈Γ(i) ηij xjℓ , and the fact that
i + ρiℓ − xiℓ ρiℓ −
the second term is at least −|ρiℓ − j∈Γ(i) ηij xjℓ |, as xi ∈ [0, 1].
Next, note that the maximum of the form Q(x) over [0, 1]nk has to occur at a boundary point,
since for any fixing of variables other than the ith group of variables xi1 , · · · , xik for a given i, the
24

form reduces to a linear functionP
of xiℓ , 1 ≤ ℓ ≤ k, which attains maximum at one of the boundaries
when subject to the constraint ℓ xiℓ = 1. Using this observation repeatedly for i ∈ [n] lets us
conclude that there is a y ∈ {0, 1}nk such that Q(y) ≥ Q(x). Since any such y corresponds to a
partitioning, and Q(y) corresponds to its objective value, the claim follows.
This completes the proof of Theorem 9.2.
As in the case of MAXCUT, we can show that the estimation procedure can be used for
estimating both in the original graph (with uniform probabilities qi ), and with the graph H, with
sampling probabilities qi /pi . We thus skip stating these claims formally.

9.2

Induced Linear Programs for Correlation Clustering

We next need to prove that the EstC procedures have approximately the same values on G and H
(with appropriate γ’s). To show this, we consider a sample (S, S ′ ) drawn as before, and show that
max

(A1 ,··· ,Ak ):S

LPAγ 1 ,··· ,Ak (V ) ≥ ∆2

max

(A1 ,··· ,Ak ):S

LPAα1 ,··· ,Ak (S ′ ) − εn∆,

(18)

where γi = qi and αi = qi /pi . As before, we consider the duals of the two programs. This is the
main place in which our correlation clustering analysis differs from the one for MAXCUT. The dual
is as follows
X
X
minimize
ui +
ρiℓ ziℓ
subject to

ui +

i

X

j∈Γ(i)

i,ℓ

ηjℓ zjℓ ≥ d−
i + ρiℓ

∀i, ℓ

−1 ≤ ziℓ ≤ 1 ∀i ∈ [n], ℓ ∈ [k]
−
P The difference now is that for any vector z, the optimal choice of ui is maxℓ {di + ρiℓ −
j∈Γ(i) ηjℓ zjℓ }. This is now a maximum of k terms, as opposed to the max of 0 and one other term
in the case of MAXCUT. But once again, we can think of the dual solution as being the vector z,
and we again have ui ≤ 2di . The dual of LPAα1 ,··· ,Ak (H) can be written down similarly.
As we did earlier, we take a solution z to the dual of the LP on G, and use the same values (for
the vertices in S ′ ) as the solution to the dual on H. The objective values are now as follows.
X
X
X
DualG =
ρiℓ ziℓ +
max{d−
+
ρ
−
ηij zjℓ }
(19)
iℓ
i

DualH ≤
Here also, we have ρeiℓ =

i∈S ′ ,ℓ

ρiℓ
.
p i ∆2

ℓ

i

i,ℓ

X

ρeiℓ ziℓ +

X
i

j∈Γ(i)

max{ de−
eiℓ −
i +ρ
ℓ

X

j∈Γ(i)∩S ′

We now show that w.p. at least 1 −

max

(A1 ,...,Ak ):S

DualH ≤

1
∆2

max

(A1 ,...,Ak ):S

wij ηij zjℓ }

(20)

1
,
n4

DualG +

εn
.
∆

(21)

We can use the same trick as in the MAXCUT case, and move to Dual∗H , in which we use
∗
iℓ
ρe∗iℓ := pρi ∆
2 , weights wij as before. The proof of Lemma 6.2 applies verbaitm. Thus it suffices to
show that w.h.p. (assuming S satisfies conditions analogous to Lemma 6.4),
max

(A1 ,...,Ak ):S

Dual∗H ≤

1
∆2

max

(A1 ,...,Ak ):S

25

DualG +

εn
.
2∆

Consider the expression
Dual∗H

! 

X X
1
1
1
∗
∗
− 2 DualG =
(Yi ρeiℓ ziℓ − 2 · ρiℓ ziℓ ) + Yi ũi − 2 · ui .
∆
∆
∆
i

(22)

ℓ

We view this as two summations (shown by the parentheses), and bound them separately. The
first is relatively easy. We observe that by definition,
ρe∗iℓ =

X

j∈Γ(i)∩Cℓ

∗ η p∗
wij
ηij
ij j
= ∗ 2
qj
pi ∆

X

1
1
= ∗ 2 · ρiℓ .
qi
pi ∆

j∈Γ(i)∩Cℓ

(23)


P
ziℓ
∗
This implies that we can write the first term as iℓ ρpiℓ∗ ∆
2 Yi − pi . This will then be bounded
i
via Bernstein’s inequality.
For bounding the second term, we start with the trick of splitting it into two terms by adding
a “hybrid” term, as follows:
 X

X
X
1
ui
ui
1
Yi ũ∗i − Yi ∗ 2 +
Yi ∗ 2 − 2 · ui .
Yi ũ∗i − 2 · ui =
∆
pi ∆
pi ∆
∆
i

i

i

The second term can again be bounded using Bernstein’s inequality, using the fact that ui =
O(di ). Let us thus P
consider the first term. We can appeal to the fact | max{P1 , . . . , Pk } −
max{Q1 , . . . , Qk }| ≤ i |Pi − Qi |, to bound it by
X

Yi

i

X
ℓ

de∗−
e∗iℓ −
i +ρ

∗ =
Using (23) and wij

X

Yi

i

=

X
ℓ

X
i

Yi

X

j∈Γ(i):ηij <0

X
ℓ

X

1
p∗i p∗j ∆2

X

j∈Γ(i)∩S ′

1

p∗i ∆2

d−
i + ρiℓ −

X

j∈Γ(i)

ηij zjℓ



.

we can bound the above by

∗
Yj |ηij |wij
(1 − zjℓ ) −

j∈Γ(i):ηij <0

∗
wij
ηij zjℓ −

X X
|ηij |
c
+
Yi
jℓ
p∗i ∆2
i

∗
|ηij |wij
(1 − zjℓ )(Yj − p∗j ) +

X
i

ℓ

Yi

X
ℓ

X

j∈Γ(i):ηij >0

X

j∈Γ(i):ηij >0

∗
|ηij |wij
zjℓ (Yj − p∗j )

∗
|ηij |wij
zjℓ (Yj − p∗j )

This leads to a sum over ℓ of terms of the form
X
i

X

j∈Γ(i):ηij 6=0

∗
|ηij |wij
(1 − zjℓ )(Yj − p∗j ) .

(24)

The nice thing now is that we can appeal (in a black-box manner, using the boundedness of η
and z) to the concentration bound for quadratic functions in Section 6.2, with ε replaced by ε/k,
to conclude the desired concentration inequality. For this goal, we again use a similar conditioning
as for MaxCut, which we state it here.
“Good” conditioning. We say that a choice of Y ’s is good if for all i ∈ V , we have
X

j∈Γ(i)

∗
wij
|ηij |Yj ≤

26

ε∆ + 2di
p∗i ∆2

Lemma 9.3. Let H be the weighted graph obtained after sampling with probabilities p∗i . For any
vertex i ∈ V , we have


X
1
ε∆
+
2d
i
∗
< 4.
wij
|ηij |Yj >
Pr 
∗
2
pi ∆
n
j∈Γ(i)

Proof. Fix some i ∈ V , and consider

P

∗
j∈Γ(i) wij |ηij |Yj =

1

p∗i ∆2

P

Yj |ηij |
j∈Γ(i) p∗j

parenthesis has expectation precisely di . Thus, applying Bernstein using maxj
P
|ηij |p∗j (1−p∗j )
with j∈Γ(i)
≤ di maxj p1∗ , we have
p∗2

1
p∗j

≤

αε ∆
ε ,

together

j

j

Pr

By setting t = (di +


. The term in the



ε∆
k ),

X

j∈Γ(i)∩VH




|ηij |Yj
εt2
> di + t ≤ exp −
.
p∗j
(di + t)αε ∆

the RHS above can be bounded by
!


2
ε(di + ε∆
ε2
1
k )
≤
exp
−
< 4.
exp −
ε∆
2α
n
(2di + k )αε ∆
ε

This completes the proof, using our choice of αε .
Conditioning on the Y being good, we can obtain the concentration bound for the quadratic
function (24).
This lets us take a union bound over all possible partitions of S (of which there are at most kn ),
to obtain (21), which then completes the proof of the main result (Theorem 4.2), for correlation
clustering.

Acknowledgement
We thank Michael Kapralov for helpful discussions as we embarked upon this project.

References
[1] Pankaj K Agarwal, Sariel Har-Peled, and Kasturi R Varadarajan. Geometric approximation
via coresets. Combinatorial and computational geometry, 52:1–30, 2005.
[2] Kook Jin Ahn and Sudipto Guha. Graph sparsification in the semi-streaming model. In International Colloquium on Automata, Languages, and Programming, pages 328–338. Springer,
2009.
[3] Kook Jin Ahn, Sudipto Guha, and Andrew McGregor. Graph sketches: sparsification, spanners,
and subgraphs. In Proceedings of the 31st symposium on Principles of Database Systems, pages
5–14. ACM, 2012.
[4] KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, and Anthony Wirth.
Correlation clustering in data streams. In International Conference on Machine Learning,
pages 2237–2246, 2015.

27

[5] Nir Ailon and Zohar Karnin. A note on: No need to choose: How to get both a ptas and
sublinear query complexity. arXiv preprint arXiv:1204.6588, 2012.
[6] Noga Alon, W Fernandez De La Vega, Ravi Kannan, and Marek Karpinski. Random sampling
and approximation of max-csps. Journal of computer and system sciences, 67(2):212–243,
2003.
[7] Noga Alon, Eldar Fischer, Ilan Newman, and Asaf Shapira. A combinatorial characterization of
the testable graph properties: it’s all about regularity. SIAM Journal on Computing, 39(1):143–
167, 2009.
[8] Alexandr Andoni, Robert Krauthgamer, and Krzysztof Onak. Streaming algorithms via precision sampling. In Foundations of Computer Science (FOCS), 2011 IEEE 52nd Annual Symposium on, pages 363–372. IEEE, 2011.
[9] Alexandr Andoni, Robert Krauthgamer, and David P Woodruff. The sketching complexity of
graph cuts. arXiv preprint arXiv:1403.7058, 2014.
[10] Sanjeev Arora, David Karger, and Marek Karpinski. Polynomial time approximation schemes
for dense instances of np-hard problems. In Proceedings of the twenty-seventh annual ACM
symposium on Theory of computing, pages 284–293. ACM, 1995.
[11] Nikhil Bansal, Avrim Blum, and Shuchi Chawla. Correlation clustering. Machine Learning,
56(1-3):89–113, 2004.
[12] Boaz Barak, Moritz Hardt, Thomas Holenstein, and David Steurer.
Subsampling
mathematical relaxations and average-case complexity. In Proceedings of the Twentysecond Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’11, pages 512–531,
Philadelphia, PA, USA, 2011. Society for Industrial and Applied Mathematics. URL:
http://dl.acm.org/citation.cfm?id=2133036.2133077.
[13] Stéphane Boucheron, Gábor Lugosi, and Pascal Massart. Concentration inequalities using the
entropy method. Annals of Probability, pages 1583–1614, 2003.
[14] Graham Cormode and Shan Muthukrishnan. An improved data stream summary: the countmin sketch and its applications. Journal of Algorithms, 55(1):58–75, 2005.
[15] Devdatt P Dubhashi and Alessandro Panconesi. Concentration of measure for the analysis of
randomized algorithms. Cambridge University Press, 2009.
[16] Uriel Feige and Gideon Schechtman. On the optimality of the random hyperplane rounding
technique for max cut. Random Structures & Algorithms, 20(3):403–440, 2002.
[17] Dimitris Fotakis, Michael Lampis, and Vangelis Th Paschos. Sub-exponential approximation
schemes for csps: from dense to almost sparse. arXiv preprint arXiv:1507.04391, 2015.
[18] Alan Frieze and Ravi Kannan. The regularity lemma and approximation schemes for dense
problems. In Foundations of Computer Science, 1996. Proceedings., 37th Annual Symposium
on, pages 12–20. IEEE, 1996.
[19] Ioannis Giotis and Venkatesan Guruswami. Correlation clustering with a fixed number of clusters. In Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm,
pages 1167–1176. Society for Industrial and Applied Mathematics, 2006.
28

[20] Ashish Goel, Michael Kapralov, and Sanjeev Khanna. Graph sparsification via refinement
sampling. arXiv preprint arXiv:1004.4915, 2010.
[21] Ashish Goel, Michael Kapralov, and Ian Post. Single pass sparsification in the streaming model
with edge deletions. arXiv preprint arXiv:1203.4900, 2012.
[22] Oded Goldreich, Shari Goldwasser, and Dana Ron. Property testing and its connection to
learning and approximation. Journal of the ACM (JACM), 45(4):653–750, 1998.
[23] Hossein Jowhari, Mert Sağlam, and Gábor Tardos. Tight bounds for lp samplers, finding
duplicates in streams, and related problems. In Proceedings of the thirtieth ACM SIGMODSIGACT-SIGART symposium on Principles of database systems, pages 49–58. ACM, 2011.
[24] Michael Kapralov. Better bounds for matchings in the streaming model. In SODA, pages
1679–1697. SIAM, 2013.
[25] Michael Kapralov, Sanjeev Khanna, and Madhu Sudan. Approximating matching size from
random streams. In SODA, pages 734–751. SIAM, 2014.
[26] Michael Kapralov, Sanjeev Khanna, and Madhu Sudan. Streaming lower bounds for approximating max-cut. In SODA, pages 1263–1282. SIAM, 2015.
[27] Michael Kapralov, Sanjeev Khanna, Madhu Sudan, and Ameya Velingker. (1+ ω (1))approximation to max-cut requires linear space. In Proceedings of the Twenty-Eighth Annual
ACM-SIAM Symposium on Discrete Algorithms, pages 1703–1722. SIAM, 2017.
[28] Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, and Aaron Sidford.
Single pass spectral sparsification in dynamic streams. In Foundations of Computer Science
(FOCS), 2014 IEEE 55th Annual Symposium on, pages 561–570. IEEE, 2014.
[29] Jonathan A Kelner and Alex Levin. Spectral sparsification in the semi-streaming setting.
Theory of Computing Systems, 53(2):243–262, 2013.
[30] Dmitry Kogan and Robert Krauthgamer. Sketching cuts in graphs and hypergraphs. In Proc.
ITCS, pages 367–376. ACM, 2015.
[31] Claire Mathieu and Warren Schudy. Yet another algorithm for dense max cut: go greedy. In
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms, pages
176–182. Society for Industrial and Applied Mathematics, 2008.
[32] Morteza Monemizadeh and David P Woodruff. 1-pass relative-error lp-sampling with applications. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms,
pages 1143–1160. SIAM, 2010.
[33] Mark Rudelson and Roman Vershynin.
Sampling from large matrices:
An approach through geometric functional analysis.
J. ACM, 54(4), July 2007.
URL:
http://doi.acm.org/10.1145/1255443.1255449, doi:10.1145/1255443.1255449.

29

