Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions
Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani,, Junxing Wang

TL;DR
This paper introduces a new short cycle decomposition framework for graphs, enabling faster algorithms for resistance approximation, spectral sketching, and graph sparsification, with applications to directed Laplacian systems.
Contribution
It presents a novel short cycle decomposition method and applies it to improve algorithms for resistance estimation, spectral sketching, and sparsification.
Findings
Faster resistance approximation algorithm with $m^{1+o(1)} ext{ time}$
Existence of spectral sketches preserving quadratic forms
Construction of nearly-linear size degree-preserving spectral sparsifiers
Abstract
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…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Advanced Graph Theory Research · Machine Learning and Algorithms
