statnet

This website provides information on, background material for and access to the statnet suite of packages for network analysis. Directions for downloading statnet can be found under Installation on the navigation bar to the left. The packages are written for the R statistical computing environment, so it runs on any computing platform that supports R. If you do not already have R installed, you will need to install it via the main R web resource-site, www.r-project.org. Instructions for installing R can also be found under Installation.

The main resources on this site are:

• Installation : Instructions on how to install and use the statnet packages and the R program.
• Online Users Guide: Manuals providing background on the theoretical, statistical and computational foundation of statnet, and a tutorial for getting started with the statnet packages.
• Resources: A tutorial used at our training workshops, and other workshop presentations that provide an overview of the statistical models, software, some example analyses, a glossary of terms and a bibliography.
• statnet Users Group: Looking for help? This is the place. Instructions for joining the statnet users group.
• Citation: If you use statnet, please cite it. All the information you need can be found here.
• License and source code attribution requirements that govern the use of the statnet packages.

What is statnet ?

statnet is a suite of software packages for network analysis that implement recent advances in the statistical modeling of networks. The analytic framework is based on Exponential family Random Graph Models (ergm). statnet provides a comprehensive framework for ergm-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm.

statnet has a different purpose than the excellent packages UCINET or Pajek; the focus is on statistical modeling of network data. The statistical modeling capabilities of statnet include ERGMs, latent space and latent cluster models.  The packages are written in a combination of (the open-source statistical language) R and (ANSI standard) C, and are called from the R command line. And because it runs in the R package (www.r-project.org), you also have access to the full functionality of R, including the packages "network" and "sna" written by Carter Butts.  statnet has a command line interface, not a GUI, with a syntax that resembles R.

statnet comprises a set of required and optional packages.

Required Packages

• ergm is a collection of functions to fit, simulate from, plot and evaluate exponential family random graph models. The main functions within the ergm package are ergm, a function to fit exponential-family random graph models in which the probability of a network is dependent upon a vector of network statistics specified by the user; simulate, a function to simulate random networks using an ERGM; and gof, a function to evaluate the goodness of fit of an ERGM to the data. ergm contains many other functions as well; for a guide to the basic types of functionality these functions provide, see Hunter et al. (2008), Morris, Handcock, and Hunter (2008), and Goodreau et al. (2008). The ergm package also contains some of the classic network datasets (including Sampson's monastery data and the Padgett Florentine networks). The list of available datasets can be found by typing the R command: data(). The datasets are listed at the end of the output.

• network is a package to create, store, modify and plot the data in network objects. The network object class, defined in the network package (Butts, Handcock, and Hunter 2007; Butts 2008), can represent a range of relational data types and it supports arbitrary vertex / edge / network attributes. Data stored as network objects can then be analyzed using all of the component packages in the statnet suite.

Optional Packages

• sna: A set of tools for traditional social network analysis (Butts 2008).

• degreenet: A package for the statistical modeling of degree distributions of networks (Handcock 2003b). It includes power-law models such as the Yule and Waring, as well as a range of alternative models that have been proposed in the literature.

• latentnet: A package to fit and evaluate latent position and cluster models for statistical networks based on Hoff, Raftery, and Handcock (2002) and Handcock, Raftery, and Tantrum (2007). The probability of a tie is expressed as a function of distances between these nodes in a latent space as well as functions of observed dyadic level covariates. For details about this package, see Krivitsky and Handcock (2008).

• netperm: A package for permutation models for relational data (Butts 2006). It provides simulation and inference tools for exponential families of permutation models on relational structures.

• networksis: A package to simulate bipartite networks with fixed marginals through sequential importance sampling (Admiraal and Handcock 2007).

• degreenet: This package was developed for the degree distributions of networks. It implements likelihood-based inference, bootstrapping, and model selection, and it includes power-law models such as the Yule and Waring as well as a range of alternative models that have been proposed in the literature. (Handcock 2003b). The theory behind these methods is described in Jones and Handcock (2003a,b); Handcock and Jones (2004, 2006).

Information on these packages is available in the online users guide and in their runtime help files. These packages are installed during the standard statnet installation process, or during any update. These packages also require other R packages for their use that are automatically installed during the installation process.

Additional optional packages for visualization are available on request from Skye Bender de-Moll.

• dynamicnetwork: A set of tools for visualizing dynamically changing networks (Bender-deMoll, Morris, and Moody 2008).

• rSonia: Provides a set of methods to facilitate exporting data and parameter settings and launching SoNIA, which stands for Social Network Image Animator (Bender-deMoll and McFarland 2003). SoNIA facilitates interactive browsing of dynamic network data and exporting animations as a QuickTime Apple (1999) movies.

References

• Admiraal R, Handcock MS (2008). networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling. Journal of Statistical Software, 24(8). http://www.jstatsoft.org/v24/i08/.

• Admiraal R, Handcock MS (2007). networksis: Simulate bipartite graphs with fixed marginals through sequential importance sampling. Statnet Project, Seattle, WA. Version 1, http://www.statnetproject.org.

• Bender-deMoll S, Morris M, Moody J (2008). Prototype Packages for Managing and Animating Longitudinal Network Data: dynamicnetwork and rSoNIA. Journal of Statistical Software, 24(7). http://www.jstatsoft.org/v24/i07/.

• Besag, J., 1974, Spatial interaction and the statistical analysis of lattice systems (with discussion), Journal of the Royal Statistical Society, B, 36, 192-236.

• Boer P, Huisman M, Snijders T, Zeggelink E (2003). StOCNET: an open software system for the advanced statistical analysis of social networks. Groningen: ProGAMMA / ICS, version 1.4 edition.

• Butts CT (2006). netperm: Permutation Models for Relational Data. Version 0.2, http://erzuli.ss.uci.edu/R.stuff.

• Butts CT (2007). sna: Tools for Social Network Analysis. Version 1.5, http://erzuli.ss.uci.edu/R.stuff.

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

• Butts CT (2008). Social Network Analysis with sna. Journal of Statistical Software, 24(6). http://www.jstatsoft.org/v24/i06/.

• Butts CT, Hunter DH, Handcock MS (2007). network: Classes for Relational Data. Version 1.2, http://erzuli.ss.uci.edu/R.stuff.

• Frank, O., and Strauss, D.(1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842.

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

• Goodreau SM, Kitts J, Morris M (2008b). Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks. Demography, 45, in press.

• Handcock, M. S. (2003) Assessing Degeneracy in Statistical Models of Social Networks, Working Paper No. 39, Center for Statistics and the Social Sciences, University of Washington. www.csss.washington.edu/Papers/wp39.pdf

• Handcock MS (2003b). degreenet: Models for Skewed Count Distributions Relevant to Networks. Statnet Project, Seattle, WA. Version 1.0, http://www.statnetproject.org.

• Handcock MS, Hunter DR, Butts CT, Goodreau, SM, Morris M (2008). statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data. Journal of Statistical Software, 24(1). http://www.jstatsoft.org/v24/i01/.

• Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003a). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Statnet Project, Seattle, WA. Version 2, http://www.statnetproject.org.

• Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003b). statnet: Software Tools for the Statistical Modeling of Network Data. Statnet Project, Seattle, WA. Version 2, http://www.statnetproject.org.

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

• 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). http://www.jstatsoft.org/v24/i03/.

• Krivitsky PN, Handcock MS (2007). latentnet: Latent position and cluster models for statistical networks. Seattle, WA. Version 2, http://www.statnetproject.org.

• Krivitsky PN, Handcock MS (2008). Fitting Latent Cluster Models for Social Networks with latentnet. Journal of Statistical Software, 24(5). http://www.jstatsoft.org/v24/i05/.

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

• Strauss, D., and Ikeda, M.(1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85, 204-212.