The project

• The beginning
• The name
• The team
• History
• Today
• Future
• References

The beginning

In 1998, Frans Stokman, in collaboration with Tom Snijders and Marijtje van Duijn (ICS/University of Groningen, the Netherlands), received a grant from the Dutch National Science Foundation (NWO) for the development of an open software system for the statistical analysis of social networks.

In March 1999, the project started under the initial title "XNET" and received project number NWO-405-20-20.

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The name

The name of the project has changed to StOCNET in the meantime. Main reason was the large number of undesired hits when searching for XNET on the world wide web. StOCNET stands for StOChastic NETworks to emphasize the fact that the main focus is on stochastic and statistical methods. All capital letters read SOCNET to refer to the main discussion list of people in the field of SOCial NETworks. The proper reference to StOCNET is

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

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The team

From the start of the project up to August 2001, Evelien Zeggelink was the leader of the project. In August she stopped, and from September 1st, 2001, to July, 2002, the project leader was Mark Huisman, under supervision of Tom Snijders and Frans Stokman. Since July 2002, the primary responsible for the project is Tom Snijders, assisted by Christian Steglich. This part of the project takes place at the ICS, University of Groningen.
The main programmer was Peter Boer, working at ProGAMMA, Groningen (now continued under the name Science Plus).

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History

Since the beginning of the project, the main focus has been on the development of an interface as general as possible for most of the methods that were to be included. For this purpose a questionnaire was developed that has been sent to what we thought were potential contributors. In the meantime, most of these contributors have reacted positively and have filled out the questionnaire. On the basis of their answers to the standard questionnaire we have been working on the main interface.

Subsequently we have implemented the following five programs,

• SIENA, for the statistical estimation of models for the evolution of social networks according to the dynamic actor-oriented model of Snijders (2001) and Steglich et al. (2004), and for MCMC estimation of exponential random graph models according to Snijders (2002);
  there is a special web page for SIENA;
• BLOCKS, designed for stochastic blockmodeling of relational data according to the methods described in Nowicki & Snijders (2001);
• p2, for the estimation of models for testing effects of actor variables and dyadic variables on the ties in a network, controlling for reciprocity and for the dispersion of the in- and of the out-degrees, according to the p2 model of van Duijn (1995), also see Lazega and van Duijn (1997) and van Duijn, Snijders and Zijlstra (2004);
• Ultras, for the analysis of undirected network data using ultrametric (i.e., hierarchical clustering) measurement models according to the methods of Schweinberger and Snijders (2003),
• and ZO for the analysis of directed and undirected graphs with given degrees according to the methods of Snijders (1991) and Molloy & Reed (1995).

The developments were presented first at the XX International Social Networks Conference in Vancouver, Canada, April 2000 and then (with a paper that can be downloaded) at the XXII International Sunbelt Social Network Conference, New Orleans, USA, February 2002.

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Today

In November 2000 this site was activated and since then it has been updated regularly. The newest version of StOCNET can now be downloaded from this site.

The first phase of the StOCNET, based on support from NWO project 405-20-20, was achieved April 2003 with the release of version 1.4.
Since then activities have continued. Version 1.5 has had several phases in the period May 2004-January 2005, all being provisional in nature.

In February 2005, the new version 1.6 was released. It includes, in addition to the earlier modules,

• the PACNET program for constructing algebras for complete social networks (Pattison et al., 2000)
• and the renewed version 2.1 of SIENA which also can analyse the joint dynamics of networks and individual attributes (Steglich et al. 2004).

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Future

We are still in the process of extending the StOCNET software. New modules can be added, and for several of the existing modules (notably p2 and Siena), active research is continuing and will lead to further extensions and improvements. We hope to continue with adding new developments to the StOCNET program.

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References

• Boer, P., Huisman, M., Snijders, T.A.B., & Zeggelink, E.P.H. (2003). StOCNET: an open software system for the advanced statistical analysis of social networks. Version 1.4. Manual. Groningen: ProGAMMA / ICS.
• Huisman, M., & Snijders, T.A.B. (2003). Statistical analysis of longitudinal network data with changing composition. Sociological Methods & Research, 32 (2003), 253-287.
• Huisman, M., & van Duijn, M.A.J. (2003). StOCNET: Software for the statistical analysis of social networks. Connections, 25(1), 7-26.
• Huisman, M., & van Duijn, M.A.J. (2004). Software for statistical analysis of social networks. Paper presented at the 6th International Conference on Logic and Methodology (RC33), Amsterdam, August 16-20, 2004.
• Lazega, E., & van Duijn, M.A.J. (1997). Position in formal structure, personal characteristics and choices of advisors in a law firm: a logistic regression model for dyadic network data. Social Networks, 19, 375-397.
• Molloy, M. & Reed, B. (1995). A critical point for random graphs with a given degree sequence. Random Structures and Algorithms, 6, 161-179.
• Nowicki, K. & Snijders, T.A.B. (2001). Estimation and prediction for stochastic block models. Journal of the American Statistical Association, 96, 1077-1087.
• Pattison, P., Wasserman, S., Robbins, G., & Kanfer, A. (2000). Statistical evaluation of algebraic constraints for social networks. Journal of Mathematical Psychology, 44, 536-568.
• Schweinberger, M. & Snijders, T.A.B. (2003). Settings in social networks: A measurement model. Sociological Methodology 2003, 307-341.
• Snijders, T.A.B. (1991). Enumeration and simulation methods for 0-1 matrices with given marginals. Psychometrika, 56, 397-417.
• Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology - 2001, 361-395.
• Snijders, T.A.B. (2002). Markov Chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, Vol. 3, No. 2.
• Steglich, C.E.G., Snijders, T.A.B. & Pearson, M. (2004). Dynamic Networks and Behavior: Separating Selection from Influence. Submitted for publication.
• van Duijn, M.A.J. (1995). Estimation of a random effect model for directed graphs.
• van Duijn, M.A.J., Snijders, T.A.B., & Zijlstra, B.H. (2004). p2: a random effects model with covariates for directed graphs. Statistica Neerlandica, 58, 234-254.