12 research outputs found
Density Independent Algorithms for Sparsifying k-Step Random Walks
We give faster algorithms for producing sparse approximations of the transition matrices of k-step random walks on undirected and weighted graphs. These transition matrices also form graphs, and arise as intermediate objects in a variety of graph algorithms. Our improvements are based on a better understanding of processes that sample such walks, as well as tighter bounds on key weights underlying these sampling processes. On a graph with n vertices and m edges, our algorithm produces a graph with about nlog(n) edges that approximates the k-step random walk graph in about m + k^2 nlog^4(n) time. In order to obtain this runtime bound, we also revisit "density independent" algorithms for sparsifying graphs whose runtime overhead is expressed only in terms of the number of vertices
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions
We develop a framework for graph sparsification and sketching, based on a new
tool, short cycle decomposition -- a decomposition of an unweighted graph into
an edge-disjoint collection of short cycles, plus few extra edges. A simple
observation gives that every graph G on n vertices with m edges can be
decomposed in time into cycles of length at most , and at most
extra edges. We give an time algorithm for constructing a
short cycle decomposition, with cycles of length , and
extra edges. These decompositions enable us to make progress on several open
questions:
* We give an algorithm to find -approximations to effective
resistances of all edges in time , improving over
the previous best of .
This gives an algorithm to approximate the determinant of a Laplacian up to
in time.
* We show existence and efficient algorithms for constructing graphical
spectral sketches -- a distribution over sparse graphs H such that for a fixed
vector , we have w.h.p. and
. This implies the existence of
resistance-sparsifiers with about edges that preserve the
effective resistances between every pair of vertices up to
* By combining short cycle decompositions with known tools in graph
sparsification, we show the existence of nearly-linear sized degree-preserving
spectral sparsifiers, as well as significantly sparser approximations of
directed graphs. The latter is critical to recent breakthroughs on faster
algorithms for solving linear systems in directed Laplacians.
Improved algorithms for constructing short cycle decompositions will lead to
improvements for each of the above results.Comment: 80 page
Graph Sketching Against Adaptive Adversaries Applied to the Minimum Degree Algorithm
Motivated by the study of matrix elimination orderings in combinatorial
scientific computing, we utilize graph sketching and local sampling to give a
data structure that provides access to approximate fill degrees of a matrix
undergoing elimination in time per elimination and
query. We then study the problem of using this data structure in the minimum
degree algorithm, which is a widely-used heuristic for producing elimination
orderings for sparse matrices by repeatedly eliminating the vertex with
(approximate) minimum fill degree. This leads to a nearly-linear time algorithm
for generating approximate greedy minimum degree orderings. Despite extensive
studies of algorithms for elimination orderings in combinatorial scientific
computing, our result is the first rigorous incorporation of randomized tools
in this setting, as well as the first nearly-linear time algorithm for
producing elimination orderings with provable approximation guarantees.
While our sketching data structure readily works in the oblivious adversary
model, by repeatedly querying and greedily updating itself, it enters the
adaptive adversarial model where the underlying sketches become prone to
failure due to dependency issues with their internal randomness. We show how to
use an additional sampling procedure to circumvent this problem and to create
an independent access sequence. Our technique for decorrelating the interleaved
queries and updates to this randomized data structure may be of independent
interest.Comment: 58 pages, 3 figures. This is a substantially revised version of
arXiv:1711.08446 with an emphasis on the underlying theoretical problem