Fast Sequential Importance Sampling to Estimate the Graph Reliability Polynomial

The reliability polynomial of a graph counts its connected subgraphs of various sizes. Algorithms based on sequential importance sampling (SIS) have been proposed to estimate a graph's reliability polynomial. We develop an improved SIS algorithm for estimating the reliability polynomial. The new algorithm runs in expected time O(mlogn alpha(m,n)) at worst and a parts per thousand m in practice, compared to I similar to(m (2)) for the previous algorithm. We analyze the error bounds of this algorithm, including comparison to alternative estimation algorithms. In addition to the theoretical analysis, we discuss methods for estimating the variance and describe experimental results on a variety of random graphs.