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.Comment: 15 pages, 14 figure, technical report for ICDE2013 paper. Appendix
has all the theorems' proofs; ICDE'201
