A framework for statistical network modeling

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.

31 pages, 1 figure

References in corpus (8)

• Cooperative Game Theory Approaches for Network Partitioning
• Hierarchical structure and the prediction of missing links in networks
• Stochastic blockmodels and community structure in networks
• Sparse exchangeable graphs and their limits via graphon processes
• The Class of Random Graphs Arising from Exchangeable Random Measures
• Edge exchangeable models for network data
• Relational exchangeability
• Exchangeable Markov processes on graphs: Feller case

Cited by in corpus (10)

• Concentration and consistency results for canonical and curved exponential-family models of random graphs
• Edge exchangeable models for network data
• Sampling perspectives on sparse exchangeable graphs
• Using Embeddings to Correct for Unobserved Confounding in Networks
• Consistent structure estimation of exponential-family random graph models with block structure
• Relational exchangeability
• Hierarchical network models for structured exchangeable interaction processes
• Community detection for interaction networks
• Projective, Sparse, and Learnable Latent Position Network Models
• A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure