Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions [article]

Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, Junxing Wang
2018 arXiv   pre-print
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 O(mn) time into cycles of length at most 2 n, and at most 2n extra edges. We give an m^1+o(1) time algorithm for constructing a short cycle decomposition, with cycles of length
more » ... ), and n^1+o(1) extra edges. These decompositions enable us to make progress on several open questions: * We give an algorithm to find (1±ϵ)-approximations to effective resistances of all edges in time m^1+o(1)ϵ^-1.5, improving over the previous best of Õ({mϵ^-2,n^2 ϵ^-1}). This gives an algorithm to approximate the determinant of a Laplacian up to (1±ϵ) in m^1 + o(1) + n^15/8+o(1)ϵ^-7/4 time. * We show existence and efficient algorithms for constructing graphical spectral sketches -- a distribution over sparse graphs H such that for a fixed vector x, we have w.h.p. x'L_Hx=(1±ϵ)x'L_Gx and x'L_H^+x=(1±ϵ)x'L_G^+x. This implies the existence of resistance-sparsifiers with about nϵ^-1 edges that preserve the effective resistances between every pair of vertices up to (1±ϵ). * 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.
arXiv:1805.12051v1 fatcat:6jz5yc74srfydjhrcept4ohwnm