StOCNET : Suite of programs for analysis of empirical network data, with the purpose of statistical inference: interpret data by applying probability models. Focus here: SIENA : for longitudinal (“panel”) data. Others programs include p2, Blocks, Ultras, ZO, Pacnet.

Modeling longitudinal network data important because empirical network structure tends to be “contaminated” by contingent events in the past, and the changes in network structures show more about general underlying social principles than one-shot network observations. Statistical methodologies enable researchers to strive after empirical testing of substantive theories and after empirical generalizations; this has not been widespread in social network analysis.

Features of Siena: Currently only method & program with this purpose Simulation-based (MCMC) methods: computer-intensive Uses network data: longitudinal; possibly multiple parallel (“samples”) networks; Development by statisticians; use by (mainly) social scientists. Complicated modeling : requires expertise of statistical modeling and of social science, therefore interdisciplinary collaboration. Collaboration exists with many substantive scientists from many social science disciplines Currently: stand-alone program; hopefully user-friendly Ongoing work: methodology far from finished

How could cyber infrastructure help? Computer intensive: need for a lot of processing (e.g. parallel), especially for modeling large networks Integration in user interface with other facilities (data transformation and structuring, visualization, other analysis methods) would be great advance Data collection Access to publicly available data Access to software √, tutorials Discussion groups √

Expertise bottlenecks: Application of these methods presupposes methodologically sophisticated users or close interaction with expert methodologists. Computer programming up to now done by statisticians themselves. This is suboptimal. But for programming these methods it is good to have not only programming skills but also basic insights in the statistical methodology and the “mind of the users”. Further development and dissemination, at various levels, is necessary.