The analysis of social networks: From description to statistical modelling (POSTPONED)

Our workshop is currently postponed until further notice, new dates for this are yet to be determined.

Contents and objectives

Data typically collected in the social sciences rely on the familiar case-by-variable research design, where "cases" (rows) represent various kinds of social actors, and "variables" (columns) contain measurements on a set of attributes of the actors or their context. Quantitative research based on this design typically emphasizes relations among the "variables." Social network research, by contrast, focuses on relations among the "cases." This change of perspective requires the development of specialized models and methods to represent, describe and analyze relational data. The course starts by introducing the basic theoretical and conceptual background of social network research, the fundamental ideas underlying the network approach, and discusses its many domains of empirical application. The course then proceeds to examine the basic analytical concepts needed to describe and understand the structure of social networks across various levels of analysis. Participants will learn how to visualize social network data to discover their main structural features, and how to implement different types of network research designs and approaches to data collection. The course also introduces contemporary statistical models for social networks, so that participants may learn how to test hypotheses using network data. Permutation tests (QAP), Exponential Random Graphs models (ERGMs) and Stochastic Actor-oriented Models (SAOMs) will be introduced as examples of statistical models for studying network structure and connective behavior. The course will include practical examples and hands-on computer laboratories based on the analysis of real-life relational data. In the laboratories, the emphasis will be on the analysis of social networks in structured social and economic settings such as, for example, business companies, and other formal organizations like hospitals, universities and other educational institutions. Students will also be given the opportunity to work with their own data and consult privately with the instructors about their own research work and problems.

Software resources

The software packages that will be introduced during the workshop include Statnet, Visone, PNet and RSIENA. The software resources used in the course are all publicly and freely available. Depending on the interests of the participants, specialized software resources developed for the R environment may also be illustrated.

Prerequisites

Participants taking this course are expected to be familiar with the basic concepts of descriptive statistics, and have an active interest in statistical inference. The basic elements of the R programming language needed to specify, estimate, and interpret network models will be introduced in the early stages of the seminar.

Instructors

Alessandro Lomi is a Distinguished Research Professor at the University of Exteter, and a professor at the University of Lugano (Switzerland) where he is a member of the Institute of Computational Science. He is a Senior Research Fellow in the School of Psychological Sciences of the University of Melbourne, and a Life Member of Clare Hall College, University of Cambridge. In the recent past, he was an elected member of the Swiss National Science Foundation, and a Jemolo Research Fellow at Nuffield College, University of Oxford. He holds a PhD from Cornell University (New York).

Viviana Amati is a postdoctoral researcher at the Social Networks Lab, ETH Zurich. She received her Ph.D. in Statistics from the University of Milano-Bicocca and has previously worked as a postdoctoral researcher at the University of Konstanz. Her primary research interest is statistical analysis and modelling of dynamic networks with a focus on estimation and misspecification of stochastic models for relational data.

Bibliography: General references

• Amati, V., Lomi, A. and Mira, A., 2018. Social network modeling. Annual Review of Statistics and Its Application, 5, pp.343-369.
• Borgatti, S.P., Mehra, A., Brass, D.J. and Labianca, G., 2009. Network analysis in the social sciences. Science, 323(5916), pp.892-895.
• Breiger, R.L. 2004.The Analysis of Social Networks. In Handbook of Data Analysis (pp. 505-526), edited by Melissa Hardy and Alan Bryman. London: SAGE Publications
• Brandes, U., Robins, G., McCranie, A., & Wasserman, S., 2013. What is network science?. Network Science, 1(1), 1-15.
• Butts, C.T., 2008. Social network analysis: A methodological introduction. Asian Journal of Social Psychology, 11(1), pp.13-41.
• Lusher, D., Koskinen, J. and Robins, G. eds., 2013. Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.
• Hennig, M., Brandes, U., Pfeffer, J., and Mergel, I., 2012. Studying social networks: A guide to empirical research. Campus Verlag.
• Robins, G. 2015. Doing Social Networks Research: Network Research Design for Social Scientists. Sage.
• Snijders, T.A., 2011. Statistical models for social networks. Annual review of sociology, 37, pp.131-153.
• Snijders, T.A., Van de Bunt, G.G. and Steglich, C.E., 2010. Introduction to stochastic actor-based models for network dynamics. Social networks, 32(1), pp.44-60.