This course introduces information and data collection methods used by social scientists working on social networks.
Associate Prof Malcolm Alexander is one of Australia’s leading sociologists working in the area of social network analysis and mathematical sociology. He made intensive studies of Australian business elite networks of the 1990s directed to public issues of corporate governance and investor capitalism. In recent years he has developed network analysis in new directions through his focus on 2-mode network mapping and investigations of elite networks in the civic cultures of Australian cities. He has published numerous articles in sociological journals, is the editor of two books and was also an editorial member of the Journal of Sociology and executive member and Treasurer of The Australian Sociological Association.
This course familiarises participants with the principal software packages used in social network analysis (UCINET and Pajek) and provides hands-on experience of working with these packages. Participants will carry out a small network project of their own to develop a real familiarity with methods and techniques of social network research and data analysis.
Participant project templates:
Participants can choose a small project from the following types of study:
- Sociometric data in bounded groups;
- Mapping personal (ego) networks;
- Participation in community organisations (Volunteering);
- Geodesic paths in ‘small world’ networks;
- Event attendance and clique formation.
Monday am: Overview and scoping of the course; Collecting sociometric data; Collecting 2-mode data.
Monday pm: Introduction to UCINET (and NetDraw); data entry formats (VNA standard); Layouts for network visualisation; Colours and shapes. Drawing complex network diagrams.
Tuesday am: Data organisation and management; Exporting from Excel or Access to UCINET; Data sources for network analysis.
Tuesday pm: Complex network diagrams (extension); Dealing with large datasets; Visualisation and its limits.
Wednesday am: Varieties of social network research; the sociometric legacies; ego network studies, sample surveys and network analysis.
Wednesday pm: Random graphs and geodesics (‘small world’ network architecture); exploring large datasets with visualisations.
Thursday am: Personal networks and community studies; Probability samples and networks (including ‘small world’ studies and 2-mode data).
Thursday pm: Using SNA metrics to explore large (random) graphs.
Friday am: Scientific interest in networks, how does it relate to social network analysis. (Plus consultations with the instructor.)
Friday pm: Mini-conference: Participants present a short account of the particular project they have worked on through the course.
This course is run in a computer lab. Equipment will be provided.
No prior knowledge of social networks analysis is required. Participants should be comfortable with using spreadsheets (Excel) and have some social science background.
- Scott, J. (2000). Social network analysis : a handbook. London ; Newbury Park, Calif., SAGE Publications.
- Buchanan, M. (2002). Nexus : small worlds and the groundbreaking science of networks. New York, W.W. Norton.
- Borgatti, S. P., M. G. Everett, et al. (2002). Ucinet 6 for Windows: Software for social network analysis. Harvard, Analytic Technologies. Manual; User Guide. www.analytictech.com
- Pajek: Program for Large Network Analysis: at http://vlado.fmf.uni-lj.si/pub/networks/pajek/
- Nooy, W. d., A. Mrvar, et al. (2005). Exploratory social network analysis with Pajek. New York, Cambridge University Press
Q: Was this course run under another name?
A: This course was previously called 'Introduction to Social Network Research and Network Analysis'
Q: What is social network analysis?
A: is a strategy for investigating social structures through the use of network and graph theories. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties or edges (relationships or interactions) that connect them.
Malcolm explains things so well/clearly! (Summer 2016)
New methods for analysis & presenting complex relationships. (Summer 2016)
Well balanced in terms of ”hands on” time compared with face to face teaching. (Summer 2016)
Yes, a good mix of lecture and computer use (Winter 2015)
Great intro to the research skills I need and helped enormously shaping my project. (Summer 2015)
Very useful instruction that will allow further substantial self study. (Spring 2014)
I can see very well how to apply many things I’ve learnt to my job. (Summer 2014)
I’ve learnt a lot about UC Net and Netdraw, about its use and capability. (Summer 2014)
The instructor's bound, book length course notes will serve as the course texts.