activity
20192021
most citedDynamic Streaming Spectral Sparsification in Nearly Linear Time and Space

9 citations · 10 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ML2021

Streaming Belief Propagation for Community Detection

Yuchen Wu, MohammadHossein Bateni, Andre Linhares +4

The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities". There has been substantial recent progress in charac…

cs.DS2021

Spectral Hypergraph Sparsifiers of Nearly Linear Size

Michael Kapralov, Robert Krauthgamer, Jakab Tardos +1

Graph sparsification has been studied extensively over the past two decades, culminating in spectral sparsifiers of optimal size (up to constant factors). Spectral hypergraph spars…

cs.DS2020

Communication Efficient Coresets for Maximum Matching

Michael Kapralov, Gilbert Maystre, Jakab Tardos

In this paper we revisit the problem of constructing randomized composable coresets for bipartite matching. In this problem the input graph is randomly partitioned across playe…

cs.DS20201 cited

Towards Tight Bounds for Spectral Sparsification of Hypergraphs

Michael Kapralov, Robert Krauthgamer, Jakab Tardos +1

Cut and spectral sparsification of graphs have numerous applications, including e.g. speeding up algorithms for cuts and Laplacian solvers. These powerful notions have recently bee…

cs.LG2020

Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard +2

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive…

cs.DS2019

Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

Michael Kapralov, Slobodan Mitrović, Ashkan Norouzi-Fard +1

Given a source of iid samples of edges of an input graph with vertices and edges, how many samples does one need to compute a constant factor approximation to the maxim…