# Improving Ego-Cluster for Network Effect Measurement

arXiv: https://arxiv.org/abs/2308.05945 · HTML: https://mlsanity.com/paper/2308.05945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing](https://mlsanity.com/paper/2608.04432/) (2026)

## Abstract (arXiv)

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.