In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walk which wait for the convergence of sampling distribution to a predetermined target distribution -a waiting process that incurs a high query cost -we develop WALK-ESTIMATE, which starts with a much shorter random walk, and then proactively estimate the sampling probability for the node taken before using acceptance-rejection sampling to adjust the sampling probability to the predetermined target distribution. We present a novel backward random walk technique which provides provably unbiased estimations for the sampling probability, and demonstrate the superiority of WALK-ESTIMATE over traditional random walks through theoretical analysis and extensive experiments over real world online social networks.
Walk-estimate proposed in REF aimed to reduce the overhead of burn-in period by considering short random walks and then using acceptance-rejection sampling to adjust the sampling probability of a node with respect to its stationary distribution.
Walk, Not Wait: Faster Sampling Over Online Social Networks
ArXiv
ArXiv
Computer Science Physics