Alessandro Lomi is a professor of Organization and Management Theory at the University of Lugano and an ordinary member of the Swiss National Science Foundation. He directs the Social Network Analysis Research (SONAR) Center. His research interests include the analysis of social networks within and between organizations and the development of statistical models for the study of dynamic event networks.
Garry Robins is a Professor in the Melbourne School of Psychological Sciences at the University of Melbourne. He is a member of the International Advisory Board of the SONAR Center, co-editor of the journal Network Science, a member of the Board of the International Network for Social Network Analysis (INSNA), and former editor of the Journal of Social Structure. His research has been centred on the development of exponential random graph models for social networks, as well as a wide range of empirical and applied social network studies.
The instructors will share responsibility for the course and will present the classes together. and will be available to students outside of class for informal interaction. Participants will also be encouraged to discuss their research problems and projects with the researchers of the Social Network Analysis Research Center at USI.
Data typically collected in the social sciences are based on the familiar case-by-variable research design, where "cases" (rows) represent various kinds of agents, and "variables" (columns) contain measurements on selected attributes of the agents. Quantitative research in the social sciences is typically based on methods that emphasize relations among the "variables." Social network research, by contrast, focuses on relations among the "cases" by examining the social structure in which individual action is embedded. The methodological and substantive scope of social network research, therefore, is very general and is not restricted to individual behavior expressed through social media. Against the backdrop of these general considerations, the course starts by introducing the basic theoretical and conceptual background of social network research, the fundamental ideas of a 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. You will learn how to visualize an empirical social network and understand its important features. We will describe different types of network research designs and present methods to collect empirical data. The course goes on to outline important network analytic methods, so that you will learn how to draw conclusions from your network data. We conclude with more advanced statistical methods such as Exponential Random Graphs models (ERGMs), where you can build a statistical model for network structure. The course will include practical examples and hands-on computer laboratories based on the analysis of real-life relational data. In the computer 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. Students will be encouraged to work with their own data.
The software packages used include the UCINET software suite and PNet - a specialized software for the statistical analysis of social networks and social influence. A fully functional 90 day trial version of UCINET may be downloaded from the site of Analytic Technologies. The most recent of version of PNet will be made available to all participants.
Participants taking this course are expected to be familiar with descriptive statistics and be interested in statistical inference.