Network Cluster-Robust Inference

Understand

Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster-robust inference methods.

• Existing such methods require clusters to be asymptotically independent.
• Under mild conditions, we prove that, for this requirement to hold for network-dependent data, it is necessary and sufficient that clusters have low conductance, the ratio of edge boundary size to volume.
• This yields a simple measure of cluster quality.