Network effect is common in social network platforms. Many new features in social networks are designed to specifically create network effect to improve user engagement. For example, content creators tend to produce more when their articles and posts receive more positive feedback from followers. This paper discusses a new cluster-level experimentation methodology to measure the creator-side metrics in the context of A/B experiment. The methodology is designed to address the cases when the experiment randomization unit and the metric measurement unit are not the same, and it is a part of the overall strategy at LinkedIn to promote a robust creator community and ecosystem. The method is developed based on the widely-cited research at LinkedIn, but significantly improves the clustering algorithm efficiency and flexibility, leading to a stronger capability of the creator-side metrics measurement and increasing velocity for creator-related experiments.

翻译：网络效应在社会网络平台中普遍存在。社会网络中的许多新功能专门设计用于创造网络效应以提高用户参与度。例如，当内容创作者的文章和帖子获得来自关注者的更多积极反馈时，他们倾向于更频繁地创作内容。本文讨论了一种新的聚类级实验方法论，用于在A/B实验背景下衡量创作者侧指标。该方法论旨在解决实验随机化单元与指标测量单元不一致的情况，是LinkedIn整体战略的一部分，旨在促进健康的创作者社区和生态系统。该方法基于LinkedIn被广泛引用的研究开发，但显著提高了聚类算法的效率和灵活性，从而增强了创作者侧指标衡量的能力，并提高了创作者相关实验的执行速度。