Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family

Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, .

dnr

Description

This package provides functions for fitting lagged exponential family models on dynamic network data, simulation from the models and model diagnostics.

Funding

This package was developed with help from ARO YIP award #W911NF-14-1-0577.

References

• Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, DOI: 10.1080/10618600.2019.1594834

• Abhirup Mallik and Zack W. Almquist (2017). "An R Package for Dynamic Network Regression." Working paper. University of Minnesota.

• Zack W. Almquist and Carter T. Butts (forthcoming). "Dynamic Network Analysis with Missing Data: Theory and Methods." Statistica Sinica. doi:10.5705/ss.202016.0108.

• Zack W. Almquist and Carter T. Butts. (2013). "Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-group Blog Citation Dynamics in the 2004 US Presidential Election." Political Analysis, 21(4), 430-448.

• Zack W. Almquist and Carter T. Butts. (2014). "Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics." In Bayesian Inference in the Social Sciences. Ed. by I. Jeliazkov and X.-S. Yang. Hoboken, New Jersey: John Wiley & Sons.