R: Fit, Simulate and Diagnose Dynamic Network Models derived...

tergm-package {tergm} | R Documentation

## Fit, Simulate and Diagnose Dynamic Network Models derived from Exponential-Family Random Graph Models

### Description

`tergm` is a collection of extensions to the `ergm` package to fit, diagnose, and simulate models for dynamic networks — networks that evolve over time — based on exponential-family random graph models (ERGMs). For a list of functions type `help(package='tergm')`

### Details

When publishing results obtained using this package, please cite the original authors as described in `citation(package="tergm")`.

All programs derived from this package must cite it.

An exponential-family random graph model (ERGM) postulates an exponential family over the sample space of networks of interest, and `ergm` package implements a suite of tools for modeling single networks using ERGMs.

There have been a number of extensions of ERGMs for modeling the evolution of networks, including the temporal ERGM (TERGM) of Hanneke et al. (2010) and the separable termporal ERGM (STERGM) of Krivitsky and Handcock (2014). The latter model allows familiar ERGM terms and statistics to be reused in a dynamic context, interpreted in terms of formation and dissolution (persistence) of ties. Krivitsky (2012) suggested a method for fitting dynamic models when only a cross-sectional network is available, provided some temporal information for it is available as well.

This package aims to implement these and other ERGM-based models for network evoluation. At this time, it implements, via the `tergm` function, a general framework for modeling tie dynamics in temporal networks with flexible model specification (including (S)TERGMs). Estimation options include a conditional MLE (CMLE) approach for fitting to a series of networks and an Equilibrium Generalized Method of Moments Estimation (EGMME) for fitting to a single network with temporal information. For further development, see the referenced papers.

If you previously used the `stergm()` function in this package, please note that `stergm()` has been superceded by the new `tergm` function, and is likely to be deprecated in future releases. The `dissolution` formula in `stergm()` maps to the new `Persist()` operator in the `tergm()` function, not the `Diss()` operator.

For detailed information on how to download and install the software, go to the Statnet project website: https://statnet.org. A tutorial, support newsgroup, references and links to further resources are provided there.

### References

Hanneke S, Fu W and Xing EP (2010). Discrete Temporal Models of Social Networks. Electronic Journal of Statistics, 2010, 4, 585-605. doi: 10.1214/09-EJS548

Krackhardt, D and Handcock, MS (2006) Heider vs Simmel: Emergent features in dynamic structures. ICML Workshop on Statistical Network Analysis. Springer, Berlin, Heidelberg, 2006.

Krivitsky PN & Handcock MS (2014) A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society, Series B, 76(1): 29-46. doi: 10.1111/rssb.12014

Krivitsky, PN (2012). Modeling of Dynamic Networks based on Egocentric Data with Durational Information. Pennsylvania State University Department of Statistics Technical Report, 2012(2012-01). https://web.archive.org/web/20170830053722/https://stat.psu.edu/research/technical-report-files/2012-technical-reports/TR1201A.pdf

Butts CT (2008). network: A Package for Managing Relational Data in . Journal of Statistical Software, 24(2). https://www.jstatsoft.org/v24/i02/.

Goodreau SM, Handcock MS, Hunter DR, Butts CT, Morris M (2008a). A statnet Tutorial. Journal of Statistical Software, 24(8). https://www.jstatsoft.org/v24/i08/.

Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics, 15: 565-583

Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3). https://www.jstatsoft.org/v24/i03/.

Morris M, Handcock MS, Hunter DR (2008). Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. Journal of Statistical Software, 24(4). https://www.jstatsoft.org/v24/i04/.

[Package tergm version 4.0.2 Index]