Parallel Spectral Graph Sparsification via Low Diameter Decompositions
We present a new solver-free parallel spectral sparsification algorithm for weighted graphs that relies only on parallel low-diameter decompositions and independent sampling. This yields the first algorithmic improvement over prior, solver-free parallel sparsification approaches since Koutis (2014) and, for the first time for a practical algorithm, eliminates any dependence on the target approximation accuracy ε in the algorithm's work and depth. Our algorithm works by sub-sampling edges according to their robust connectivity, as introduced by Kapralov and Panigrahy (2012). We show how to estimate the robust connectivities of G in an extremely simple manner: we create multiple random sub graphs Gp, where each edge in G is sub-sampled independently with probability pe = min {we · p, 1}. Then, we run a Low Diameter Decomposition in each of the graphs. If u and v often share a cluster in the LDDs, then this provides us with an upper bound on the robust connectivity of the edge e = (u,v). Carefully invoking this procedure for O (log n) different values of the probabilities p then allows us to obtain sufficiently good estimates for sub-sampling. We additionally complement the theory with an experimental evaluation demonstrating strong performance across relevant graphs and sparsity regimes.