How do you handle network effects in A/B testing?

A/B testing typically assumes that the outcomes of users in the treatment and control groups are independent. However, in social platforms like Facebook or Instagram, where user actions can directly affect others, this assumption often breaks down. If a new feature boosts activity in the treatment group, that behavior can influence their friends in the control group—through likes, shares, messages, or comments—creating a spillover effect. This contamination can lead to underestimated treatment effects and invalid conclusions.

To address this, you can use cluster randomization, where instead of assigning individual users randomly, you assign entire clusters of connected users—such as social groups, messaging cohorts, or even geographic regions. This minimizes the potential for cross-group contamination. To find these clusters, apply graph partitioning algorithms like METIS or Louvain community detection. These methods help identify groups with many internal connections and few connections to users outside the group.

Once clustered, randomly assign these entire blocks to control or treatment, preserving social integrity while enabling a fair comparison. After deployment, analyze metrics at both the user and cluster level, adjusting for dependencies between groups. Also, consider tracking secondary network effects, such as changes in sharing or messaging volume, to measure indirect influence caused by the treatment.