A framework for statistical network modeling

TL;DR

This paper introduces a comprehensive framework for statistical network modeling that separates data generation from sampling, enabling more reliable inference and encompassing existing and new models.

Contribution

It proposes a unified framework decomposing network models into data generating processes and sampling mechanisms, covering current and novel models like edge exchangeable ones.

Findings

Framework encompasses all current models and new ones outside existing paradigms.

Enables development of theory and methods with sound inferential properties.

Addresses fundamental issues in statistical inference for network data.

Abstract

Basic principles of statistical inference are commonly violated in network data analysis. Under the current approach, it is often impossible to identify a model that accommodates known empirical behaviors, possesses crucial inferential properties, and accurately models the data generating process. In the absence of one or more of these properties, sensible inference from network data cannot be assured. Our proposed framework decomposes every network model into a (relatively) exchangeable data generating process} and a sampling mechanism that relates observed data to the population network. This framework, which encompasses all models in current use as well as many new models, such as edge exchangeable and relationally exchangeable models, that lie outside the existing paradigm, offers a sound context within which to develop theory and methods for network analysis.