Publications associées à Coupe (théorie des graphes)

Mikhail Kapralov, Mikhail Makarov, Jakab Tardos

A motif is a frequently occurring subgraph of a given directed or undirected graph G (Milo et al.). Motifs capture higher order organizational structure of G beyond edge relationships, and, therefore, have found wide applications such as in graph clusterin ...

IEEE COMPUTER SOC2022

An Optimal Space Lower Bound for Approximating MAX-CUT

Mikhail Kapralov

We consider the problem of estimating the value of MAX-CUT in a graph in the streaming model of computation. At one extreme, there is a trivial 2-approximation for this problem that uses only O(log n) space, namely, count the number of edges and output hal ...

ASSOC COMPUTING MACHINERY2019

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Ola Nils Anders Svensson

A sparsifier of a graph G (Bencztir and Karger; Spielman and Teng) is a sparse weighted subgraph (G) over tilde that approximately retains the same cut structure of G. For general graphs, non-trivial sparsification is possible only by using weighted graphs ...

IEEE COMPUTER SOC2019

Efficiently Learning Fourier Sparse Set Functions

Andreas Krause, Mikhail Kapralov, Amir Zandieh

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Testing Graph Clusterability: Algorithms and Lower Bounds

Mikhail Kapralov, Aidasadat Mousavifar, Ashish Hari Chiplunkar

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generali ...

IEEE COMPUTER SOC2018

Approximating Steiner trees and forests with minimum number of Steiner points

Nachshon Cohen

Let R be a finite set of terminals in a convex metric space (M, d). We give approximation algorithms for problems of finding a minimum size set S subset of M of additional points such that the unit-disc graph G[R boolean OR S] of R boolean OR S satisfies s ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2018

Round Compression for Parallel Matching Algorithms

Slobodan Mitrovic, Aleksander Madry

For over a decade now we have been witnessing the success of massive parallel computation (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. One of the reasons for their success is the fact that these frameworks are able to accurately capture th ...

ASSOC COMPUTING MACHINERY2018

Hardness Results for Consensus-Halving

Jie Zhang, Aris Filos Ratsikas

The Consensus-halving problem is the problem of dividing an object into two portions, such that each of n agents has equal valuation for the two portions. We study the epsilon-approximate version, which allows each agent to have an epsilon discrepancy on t ...

Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik2018

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Coupe (théorie des graphes)

Acceleration of graph pattern mining and applications to financial crime

Motif Cut Sparsifiers

What graph neural networks cannot learn: depth vs width

An Optimal Space Lower Bound for Approximating MAX-CUT

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Efficiently Learning Fourier Sparse Set Functions

Testing Graph Clusterability: Algorithms and Lower Bounds

Approximating Steiner trees and forests with minimum number of Steiner points

Round Compression for Parallel Matching Algorithms

Hardness Results for Consensus-Halving

Graph Chatbot

Chattez avec Graph Search

Acceleration of graph pattern mining and applications to financial crime

Motif Cut Sparsifiers

What graph neural networks cannot learn: depth vs width

An Optimal Space Lower Bound for Approximating MAX-CUT

Testing Graph Clusterability: Algorithms and Lower Bounds

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Approximating Steiner trees and forests with minimum number of Steiner points

Round Compression for Parallel Matching Algorithms

Efficiently Learning Fourier Sparse Set Functions

Hardness Results for Consensus-Halving