  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.
