This course investigates current advances in the use of network data for social research. Computer simulations and modelling have opened new vistas for statistical analysis and modelling of network data and investigations of big data. Social media research involves a new universe of work for social research. The course builds on the exploration of research approaches, methods and analytic tools in Social Network Analysis (SNA) covered in the prior, introductory course and demonstrates how these are embedded and extended in new software packages, particularly those in R. It shows how this new software makes the analysis of large datasets available to, and useful for, social researchers working with network data.
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 expands the technical procedures of the earlier course with respect to network visualisation (graph mapping), working with large datasets and ergm statistical modelling for network data. The course requires that participants are familiar with handling SNA datasets and using UCINET and NetDraw. It uses NodeXL, Gephi, Pajek, PNet and Statnet and discusses related software packages.
The course discusses and explores the principles and requirements of egonet and survey methodologies (Ron Burt), ‘whole network’ data collection and analysis for large networks and 2-mode (affiliation) data, ‘big data’ data mining issues and the prospects for whole population network research.
Session 1.1: Introductions, overview and scoping
Session 1.2: Data formats for network data and social network datasets
Session 1.3: Network visualisation packages: A survey and recommendations
Session 1.4: Core elements of network analysis and social research using network data
Session 2.1: Scaling up: The limits and limitations of UCINET
Session 2.2: Data analysis in Pajek
Session 2.3: Network packages in R: igraph, sna and StatNet
Session 2.4: SNA in R: The standard datasets and examples
Session 3.1 and 3.2: SNA in R: Big datasets
Session 3.3 and 3.4: The special world of social media data
Session 4.1: Social network research and network science
Session 4.2: The new horizons of social network research
The remaining time will be devoted to individual consultations, work and collective discussion of projects nominated by attendees.
This course is run in a computer lab. Equipment will be provided.
The ACSPRI course, Introduction to Social Network Research and Network Analysis.
The new web-based resources were very helpful. Excellent format for participatory classwork & individual work. (Winter 2014)
The course presented practical applicable information as well as theoretic grounding. Big picture concepts alongside introductions to software and structuring data, design, methods. (Winter 2014)
The instructor's bound, book length course notes will serve as the course texts.