Framework and algorithms for network bucket testing.

Bucket testing, also known as split testing, A/B testing, or 0/1 testing, is a widely used method for evaluating users' satisfaction with new features, products, or services. In order not to expose the whole user base to the new service, the mean user satisfaction rate is estimated by exposing the service only to a few uniformly chosen random users. In a recent work, Backstrom and Kleinberg, defined the notion of network bucket testing for social services. In this context, users' interactions are only valid for measurement if some minimal number of their friends are also given the service. The goal is to estimate the mean user satisfaction rate while providing the service to the least number of users. This constraint makes uniform sampling, which is optimal for the traditional case, grossly inefficient. In this paper we introduce a simple general framework for designing and evaluating sampling techniques for network bucket testing. The framework is constructed in a way that sampling algorithms are only required to generate sets of users to which the service should be provided. Given an algorithm, the framework produces an unbiased user satisfaction rate estimator and a corresponding variance bound for any network and any user satisfaction function. Furthermore, we present several simple sampling algorithms that are evaluated using both synthetic and real social networks. Our experiments corroborate the theoretical results and demonstrate the effectiveness of the proposed framework and algorithms.