  A lot of online marketing campaigns aim to promote user interaction. The
average treatment effect (ATE) of campaign strategies need to be monitored
throughout the campaign. A/B testing is usually conducted for such needs,
whereas the existence of user interaction can introduce interference to normal
A/B testing. With the help of link prediction, we design a network A/B testing
method LinkLouvain to minimize graph interference and it gives an accurate and
sound estimate of the campaign's ATE. In this paper, we analyze the network A/B
testing problem under a real-world online marketing campaign, describe our
proposed LinkLouvain method, and evaluate it on real-world data. Our method
achieves significant performance compared with others and is deployed in the
online marketing campaign.
