Understanding Human Social Networks Workshop
Understanding Human Social Networks Workshop
A five day intensive course on theories and methods for understanding human social networks: University of Melbourne, Australia, 25 – 29 June 2007
Lecturers: Professor Pip Pattison and Associate Professor Garry Robins, School of Behavioural Science, University of Melbourne.
The intensive comprises two University of Melbourne postgraduate subjects, 512-988 Introduction to Social Network Analysis and 512-989 Statistical Models for Social Networks. Taken together, the two subjects constitute an intensive five-day introduction to theories and methods for understanding human social networks and their role in social and organisational processes. The full intensive involves 36 hours of total contact time, including lectures, seminars and practical classes. It will be held at the University of Melbourne from 25-29 June: 512-988 runs from 9am, June 25 to 12.30pm, June 27 and 512-989 runs from 1.30pm, June 27 to 5pm, June 29. Students may complete one or both subjects, in either assessed or non-assessed mode.
The formal descriptions of the two subjects are attached. The first subject (512-988) will include topics on data collection approaches, and on various deterministic analytic methods, such as centrality, cohesive subsets, structural equivalence, and degree distributions. These methods will be introduced in the context of important social network theories and empirical examples, including strong and weak ties, structural holes, and influence processes through cohesion and equivalence. There will be an introduction to small world and other global network structures.
The second subject (512-989) covers statistical models for social networks. Emphasis is given to the theoretical rationale for statistical modelling approaches and to their application in empirical settings. Simple random (Bernoulli) and dyadic independence graph distributions will be presented. More advanced statistical topics will include: an introduction to exponential random graph (p*) models including simulation and fitting models to data; new specifications for exponential random graph models; models for bipartite graphs and for network evolution. Selection and influence processes, and networks in organisations, will be discussed.
Practical exercises will involve network data collection and network analysis and will utilise a range of social network software such as Pajek, UCINET, StOCNET/Siena and pnet. Participants will also have the opportunity to analyse their own data.
Assessment in each subject comprises class exercises to be completed during the instruction period for the subject (each of no more than 250 words and each worth 5%) and a final assignment of 2,000 words (80%) to be completed later in September 2007.
The pre-requisite for the introductory subject 512-988 is an undergraduate honours degree in any discipline. Postgraduate students at the University of Melbourne can enrol in either or both of 512-988 and 512-989 (subject to meeting pre-requisites), and will obtain a grade for each subject by completion of the required assessment, to be submitted by September, 2007.
External (Community Access Program) students are also welcome and can complete one or both subjects (subject to meeting pre-requisites) in either fully enrolled or audit mode (i.e., with or without assessment). The Community Access fee will be (Aust) $2,067.50/$1,110.25 (assessed/non assessed) for both subjects and (Aust) $1,033.75/$551.25 (assessed/non assessed) for each single subject.
ARC SISS Network members funded places (including transport and accommodation) are available.
For further details, contact Galina Daraganova, School of Behavioural Science, University of Melbourne, gda@unimelb.edu.au.
Details of subjects
512-988 Introduction to Social Network Analysis
Credit points: 6.25
Subject coordinators: A/Prof Garry Robins and Prof Pip Pattison
Prerequisites: An undergraduate degree at honours level (or equivalent)
Contact/Time Commitment: 18 hours of class contact time (lectures and practical classes) and a total workload of 54 hours. The subject will be taught in intensive mode in the winter break.
Subject description: The subject will introduce students to theories and methods for understanding human social networks and their role in social and organisational processes. Major theoretical and methodological developments in the field will be introduced including: social selection and social influence processes; network structure and relational content; structural holes; weak ties; social capital; networks and the micro-macro problem; egocentric and complete networks; network sampling; data collection strategies; cohesive subsets; centrality; blockmodels; connectivity; network visualisation.
Assessment: Four short class exercises during the subject (each of no more than 250 words and each worth 5%) and a final assignment of 2,000 words (80%).
Objectives and generic skills: On completion of this subject, students should be able to: (a) develop research proposals based on relational, rather than individual, conceptions of human social processes; (b) critically examine theories and concepts pertaining to social and organisational structures; and (c) understand and apply basic tools for relational analysis.
Prescribed Text: Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. Cambridge, UK: Cambridge University Press.
A reading pack will also be provided.
512-989 Statistical Models for Social Networks
Credit points: 6.25
Subject coordinators: A/Prof Garry Robins and Prof Pip Pattison
Prerequisites: 512-988 Introduction to Social Network Analysis (or equivalent)
Contact/Time Commitment: 18 hours of class contact time (lectures and practical classes) and a total workload of 54 hours. The subject will be taught in intensive mode in the winter break.
Subject description: The subject covers statistical models for social networks. Emphasis is given to the theoretical rationale for statistical modelling approaches and to their application in empirical settings. Topics will include: network sampling; simple random graph models; dyad-independent models, including latent space models and stochastic blockmodels; conditional uniform random graph distributions and the quadratic assignment procedure; exponential random graph models, including Markov random graphs, models for valued and multivariate networks, social selection and social influence models, temporal models, realisation-dependent models; network evolution models.
Assessment: Four short class exercises during the subject (each of no more than 250 words and each worth 5%) and a final assignment of 2,000 words (80%).
Objectives and generic skills: On completion of this subject, students should be able to: (a) formulate appropriate analytic approaches for research questions involving network and other relational concepts; (b) analyse network and other relational data structures using contemporary modelling techniques; and (c) understand the mathematical and statistical basis of complex analyses for dependent relational observations.
Prescribed text: A reading pack will be provided
Start Date: Monday 25th of June 2007
End Date: Friday 29th of June 2007
Last Updated: Thursday 5th of July 2007
