Faster Random Walks By Rewiring Online Social Networks On-The-Fly

TL;DR

This paper introduces MTO-Sampler, a method that rewires online social networks on-the-fly to enable faster random walks, reducing query costs for network sampling through a virtual overlay topology.

Contribution

It proposes a novel on-the-fly rewiring technique to accelerate random walks over restricted social network interfaces, improving sampling efficiency and reducing query costs.

Findings

MTO-Sampler significantly reduces the number of queries needed for sampling.

The method is effective on real-world social networks like Google Plus and Epinion.

It provably enhances the efficiency of network sampling processes.

Abstract

Many online social networks feature restrictive web interfaces which only allow the query of a user's local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chain Monte Carlo methods such as random walks to sample the social network and support analytics based on the samples. The problem with such an approach, however, is the large amount of queries often required (i.e., a long "mixing time") for a random walk to reach a desired (stationary) sampling distribution. In this paper, we consider a novel problem of enabling a faster random walk over online social networks by "rewiring" the social network on-the-fly. Specifically, we develop Modified TOpology (MTO)-Sampler which, by using only information exposed by the restrictive web interface, constructs a "virtual" overlay topology of the social network while performing a random walk, and ensures that the random walk follows the modified overlay topology rather than the original one. We show that MTO-Sampler not only provably enhances the efficiency of sampling, but also achieves significant savings on query cost over real-world online social networks such as Google Plus, Epinion etc.