The Sketching Complexity of Graph Cuts

نویسندگان

  • Alexandr Andoni
  • Robert Krauthgamer
  • David P. Woodruff
چکیده

We study the problem of sketching an input graph, so that, given the sketch, one can estimate the value (capacity) of any cut in the graph up to 1+ε approximation. Our results include both upper and lower bound on the sketch size, expressed in terms of the vertex-set size n and the accuracy ε. We design a randomized scheme which, given ε ∈ (0, 1) and an n-vertex graph G = (V,E) with edge capacities, produces a sketch of size Õ(n/ε) bits, from which the capacity of any cut (S, V \ S) can be reported, with high probability, within approximation factor (1 + ε). The previous upper bound is Õ(n/ε) bits, which follows by storing a cut sparsifier graph as constructed by Benczúr and Karger [BK96] and followup work [SS11, BSS12, FHHP11, KP12]. In contrast, we show that if a sketch succeeds in estimating the capacity of all cuts (S, S̄) in the graph (simultaneously), it must be of size Ω(n/ε) bits.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Optimal Lower Bounds for Sketching Graph Cuts

We study the space complexity of sketching cuts and Laplacian quadratic forms of graphs. We show that any data structure which approximately stores the sizes of all cuts in an undirected graph on n vertices up to a 1 + ǫ error must use Ω(n logn/ǫ) bits of space in the worst case, improving the Ω(n/ǫ) bound of [ACK16] and matching the best known upper bound achieved by spectral sparsifiers [BSS1...

متن کامل

A comparative performance of gray level image thresholding using normalized graph cut based standard S membership function

In this research paper, we use a normalized graph cut measure as a thresholding principle to separate an object from the background based on the standard S membership function. The implementation of the proposed algorithm known as fuzzy normalized graph cut method. This proposed algorithm compared with the fuzzy entropy method [25], Kittler [11], Rosin [21], Sauvola [23] and Wolf [33] method. M...

متن کامل

An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching

Let f : {−1, 1} → R be an n-variate polynomial consisting of 2 monomials, in which only s 2 coefficients are non-zero. The goal is to learn the polynomial by querying the values of f . We introduce an active learning framework that is associated with a low query cost and computational runtime. The significant savings are enabled by leveraging sampling strategies based on modern coding theory, s...

متن کامل

Learning Fourier Sparse Set Functions

Can we learn a sparse graph from observing the value of a few random cuts? This and more general problems can be reduced to the challenge of learning set functions known to have sparse Fourier support contained in some collection P. We prove that if we choose O(k log |P|) sets uniformly at random, then with high probability, observing any k-sparse function on those sets is sufficient to recover...

متن کامل

Finding All Convex Cuts of a Plane Graph in Cubic Time

In this paper we address the task of finding convex cuts of a graph. In addition to the theoretical value of drawing a connection between geometric and combinatorial objects, cuts with this or related properties can be beneficial in various applications, e. g., routing in road networks and mesh partitioning. It is known that the decision problem whether a general graph is k-convex is NP-complet...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • CoRR

دوره abs/1403.7058  شماره 

صفحات  -

تاریخ انتشار 2014