  Given a large population, it is an intensive task to gather individual
preferences over a set of alternatives and arrive at an aggregate or collective
preference of the population. We show that social network underlying the
population can be harnessed to accomplish this task effectively, by sampling
preferences of a small subset of representative nodes. We first develop a
Facebook app to create a dataset consisting of preferences of nodes and the
underlying social network, using which, we develop models that capture how
preferences are distributed among nodes in a typical social network. We hence
propose an appropriate objective function for the problem of selecting best
representative nodes. We devise two algorithms, namely, Greedy-min which
provides a performance guarantee for a wide class of popular voting rules, and
Greedy-sum which exhibits excellent performance in practice. We compare the
performance of these proposed algorithms against random-polling and popular
centrality measures, and provide a detailed analysis of the obtained results.
Our analysis suggests that selecting representatives using social network
information is advantageous for aggregating preferences related to personal
topics (e.g., lifestyle), while random polling with a reasonable sample size is
good enough for aggregating preferences related to social topics (e.g.,
government policies).
