Single Pass Spectral Sparsification in Dynamic Streams
Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford · 2014
We present the first single pass algorithm for computing spectral sparsifiers of graphs in the dynamic semi-streaming model. Given a single pass over a stream containing insertions and deletions of edges to a graph, G, our algorithm maintains a randomized linear sketch of the incidence matrix into dimension O(1/∈2npolylog(n)). Using this sketch, the algorithm can output a (1±∈) spectral sparsifier for G with high probability. While O(1/∈2npolylog(n)) space algorithms are known for computing cut sparsifiers in dynamic streams [1], [2] and spectral sparsifiers in insertion-only streams [3], prior to our work, the best known single pass algorithm for maintaining spectral sparsifiers in dynamic streams required sketches of dimension Ω(1/∈2n5/3). To achieve our result, we show that, using a coarse sparsifier of G and a linear sketch of G's incidence matrix, it is possible to sample edges by effective resistance, obtaining a spectral sparsifier of arbitrary precision. Sampling from the sketch requires a novel application of ℓ2/ℓ2sparse recovery, a natural extension of the ℓ0methods used for cut sparsifiers in [1]. Recent work of [2] on row sampling for matrix approximation gives a recursive approach for obtaining the required coarse sparsifiers. Under certain restrictions, our approach also extends to the problem of maintaining a spectral approximation for a general matrix ATA given a stream of updates to rows in A.