Statistical analysis of social networks

Description:

Summary

This is an introduction to statistical analysis of networks. While no strict prerequisites are assumed, you might find it helpful to have some basic knowledge of social network analysis beforehand. In particular, “Introduction to social network analysis using UCINET and Netdraw”, given in the preceding week (4 - 8 July 2016) in the methods@manchester Summer School provides a good background. To benefit fully from the course requires a basic knowledge of standard statistical methods, such regression analysis. The course aims to give a basic understanding of and working handle on drawing inference for structure and attributes, both cross-sectionally as well as longitudinally. A fundamental notion of the course will be how the structure of observed graphs relate to various forms of random graphs. This will be developed in the context of non-parametric approaches and elaborated to analysis of networks using exponential random graph models (ERGM) and stochastic actor-oriented models. The main focus will be on explaining structure but an outlook to explaining individual-level outcomes will be provided.

The participant will be provided with several hands-on exercises, applying the approaches to a suite of real world data sets. We will use the stand-alone graphical user interface package MPNet and R. In R we will learn how to use the packages ‘sna’, ‘statnet’, and ‘RSiena’. No familiarity with R is assumed but preparatory exercises will be provided ahead of the course.

Literature we will draw on includes:

Lusher, D., Koskinen, J., Robins, G., (2013). Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, Cambridge University Press, NY.

Snijders, Tom A. B., Gerhard G. van de Bunt, and Christian E.G. Steglich. 2010. “Introduction to stochastic actor-based models for network dynamics.” Social Networks 32:44-60.

MPNet can be downloaded from

http://www.swinburne.edu.au/business-law/research/transformative-innovation/our-research/MelNet-social-network-group/PNet-software/index.html

Course objectives

The course will:

1. Introduce how statistical evidence relates to social networks

2. Explain how to draw inference about key network mechanisms from observations

3. Provide hands-on training to use software to investigate

i. social network structure

ii. tie-formation in cross-sectional data

iii. tie-formation in longitudinal data

iv. take into account network dependencies between individuals

Course timetable

Day one

Introduction to working with networks in R

Day two

Morning – Subgraphs and null distributions and ERGM rationale

Afternoon – ERGMs and dependence

Day three

Morning – ERGM: Issues and technicalities

Afternoon – SAOM: introduction to longitudinal modelling

Day four

Morning – SAOM: introduction to longitudinal modelling

Afternoon – Extensions and further issues

Day five

Morning – Influence, contagion, and outlook to further issues.

Timetable is subject to change.

Course tutors

The course will be taught by Dr Johan Koskinen

Cost:

Students £600 | University of Manchester staff £600 | other attendees £900

Website and registration:

http://www.methods.manchester.ac.uk/events/summer-school-2016/

Region:

North West

Keywords:

Statistical Theory and Methods of Inference, Network analysis, Mixed Methods Data Handling and Data Analysis, Social Network Analysis, Interaction analysis

Related publications and presentations:

Statistical Theory and Methods of Inference
Network analysis
Mixed Methods Data Handling and Data Analysis
Social Network Analysis
Interaction analysis

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