Exponential-family Random Network Models

Abstract

Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing com- plex social phenomena. We generalize ERGM by also modeling nodal attributes as random variates, thus creating a random model of the full network, which we call Exponential-family Random Network Models (ERNM). We demonstrate how this framework allows a new formu- lation for logistic regression in network data. We develop likelihood-based inference for the model and an MCMC algorithm to implement it. This new model formulation is used to analyze a peer social network from the National Lon- gitudinal Study of Adolescent Health. We model the relationship between substance use and friendship relations, and show how the results differ from the standard use of logistic regression on network data.

References in corpus (1)

• Exponential-Family Random Graph Models for Valued Networks

Cited by in corpus (7)

• One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity
• Statistical Inference in Political Networks Research
• Analysis of Partially Observed Networks via Exponential-family Random Network Models
• Bayesian Exponential Random Graph Models with Nodal Random Effects
• Kernel-based Approximate Bayesian Inference for Exponential Family Random Graph Models
• A New Generative Statistical Model for Graphs: The Latent Order Logistic (LOLOG) Model
• An Approach to Causal Inference over Stochastic Networks