  A/B testing, also known as controlled experiment, bucket testing or splitting
testing, has been widely used for evaluating a new feature, service or product
in the data-driven decision processes of online websites. The goal of A/B
testing is to estimate or test the difference between the treatment effects of
the old and new variations. It is a well-studied two-sample comparison problem
if each user's response is influenced by her treatment only. However, in many
applications of A/B testing, especially those in HIVE of Yahoo and other social
networks of Microsoft, Facebook, LinkedIn, Twitter and Google, users in the
social networks influence their friends via underlying social interactions, and
the conventional A/B testing methods fail to work. This paper considers the
network A/B testing problem and provide a general framework consisting of five
steps: data sampling, probabilistic model, parameter inference, computing
average treatment effect and hypothesis test. The framework performs well for
network A/B testing in simulation studies.
