A/B testing is an effective way to assess the potential impacts of two treatments. For A/B tests conducted by IT companies, the test users of A/B testing are often connected and form a social network. The responses of A/B testing can be related to the network connection of test users. This paper discusses the relationship between the design criteria of network A/B testing and graph cut objectives. We develop asymptotic distributions of graph cut objectives to enable rerandomization algorithms for the design of network A/B testing under two scenarios.

翻译：A/B测试是评估两种处理潜在影响的有效方法。在信息技术企业开展的A/B测试中，测试用户往往相互连接并形成社交网络。A/B测试的响应可能与测试用户的网络关联性相关。本文探讨了网络A/B测试设计准则与图割目标之间的关系。我们推导了图割目标的渐近分布，从而能够在两种场景下为网络A/B测试设计实现重随机化算法。