93 citations · 537 across the 73 of their papers we have counts for
6 papers · 1 filter
A Framework for Analyzing Resparsification Algorithms
Rasmus Kyng, Jakub Pachocki, Richard Peng +1
A spectral sparsifier of a graph is a sparser graph that approximately preserves the quadratic form of , i.e. for all vectors , , where $…
Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs
Michael B. Cohen, Jonathan Kelner, John Peebles +4
In this paper we introduce a notion of spectral approximation for directed graphs. While there are many potential ways one might define approximation for directed graphs, most of t…
An Empirical Study of Cycle Toggling Based Laplacian Solvers
Kevin Deweese, John R. Gilbert, Gary Miller +3
We study the performance of linear solvers for graph Laplacians based on the combinatorial cycle adjustment methodology proposed by [Kelner-Orecchia-Sidford-Zhu STOC-13]. The appro…
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More
Michael B. Cohen, Jon Kelner, John Peebles +3
In this paper, we provide faster algorithms for computing various fundamental quantities associated with random walks on a directed graph, including the stationary distribution, pe…
On Fully Dynamic Graph Sparsifiers
Ittai Abraham, David Durfee, Ioannis Koutis +2
We initiate the study of dynamic algorithms for graph sparsification problems and obtain fully dynamic algorithms, allowing both edge insertions and edge deletions, that take polyl…
Scalable Constrained Clustering: A Generalized Spectral Method
Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla +2
We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacia…