Navigating the Range of Statistical Tools for Inferential Network Analysis

Abstract

The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis—the quadratic assignment procedure, exponential random graph models, and latent space network models—highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This article introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and it helps researchers choose which model to use in their own research.