Walk, Not Wait: Faster Sampling Over Online Social Networks

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.