Walk, Not Wait: Faster Sampling Over Online Social Networks
Azade Nazi, Zhuojie Zhou, Saravanan Thirumuruganathan, Nan Zhang,, Gautam Das

TL;DR
This paper presents WALK-ESTIMATE, a new method for faster node sampling in online social networks that reduces waiting time by estimating and adjusting sampling probabilities using backward random walks.
Contribution
Introduces WALK-ESTIMATE, a novel sampling technique combining short random walks with probability estimation and acceptance-rejection to improve efficiency over traditional methods.
Findings
WALK-ESTIMATE achieves faster sampling with lower query costs.
The method provides provably unbiased sampling estimates.
Experimental results show superior performance over traditional random walks.
Abstract
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.
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Taxonomy
TopicsComplex Network Analysis Techniques · Peer-to-Peer Network Technologies · Caching and Content Delivery
