Before revision: This work considers the general task of estimating the sum of a bounded function over the edges of a graph that is unknown a priori, where graph vertices and edges are built on-the-fly by an algorithm and the resulting graph is too large to be kept in memory or disk. Prior work proposes Markov Chain Monte Carlo (MCMC) methods that simultaneously sample and generate the graph , eliminating the need for storage .
After revision: This work considers the general task of estimating the sum of a bounded function over the edges of a graph , given neighborhood query access and where access to the entire network is prohibitively expensive. To estimate this sum, prior work proposes Markov Chain Monte Carlo (MCMC) methods that simultaneously sample and generate the graph , eliminating the need for storage .