The network effect, wherein one user's activity impacts another user, is common in social network platforms. Many new features in social networks are specifically designed to create a network effect, enhancing user engagement. For instance, content creators tend to produce more when their articles and posts receive positive feedback from followers. This paper discusses a new cluster-level experimentation methodology for measuring creator-side metrics in the context of A/B experiments. The methodology is designed to address cases where the experiment randomization unit and the metric measurement unit differ. It is a crucial part of LinkedIn's overall strategy to foster a robust creator community and ecosystem. The method is developed based on widely-cited research at LinkedIn but significantly improves the efficiency and flexibility of the clustering algorithm. This improvement results in a stronger capability for measuring creator-side metrics and an increased velocity for creator-related experiments.

翻译：网络效应——即一个用户的活动会影响其他用户——在社交网络平台中普遍存在。许多社交网络的新功能都专门设计用于创造网络效应，从而提升用户参与度。例如，当内容创作者的文章和帖子收到追随者的积极反馈时，他们往往会创作更多内容。本文讨论了一种在A/B实验背景下测量创作者侧指标的新型聚类级实验方法。该方法旨在解决实验随机化单元与指标测量单元不一致的情况。它是领英构建强大创作者社区与生态系统的整体战略中的关键部分。该方法是基于领英广为引用的研究开发的，但显著提高了聚类算法的效率与灵活性。这一改进带来了更强的创作者侧指标测量能力以及更快的创作者相关实验速度。