Social Network Analysis 4-Day Course: Theory, Method and Application
Join the four-day intensive social network analysis (SNA) course. Learn how to conduct social network research. Move from the fundamentals of networks to how to use cutting-edge statistical network models. Some general statistical knowledge is assumed.
You will require your own PC laptop (or Mac with Windows installed).
The course includes all course exercise materials. Lunch and afternoon tea is provided.
Day 1: Network Fundamentals
• What is distinctive about social network research?
• Network representations and network data.
• Qualitative versus quantitative data collection.
• Primary versus secondary data collection.
• Ethics and network research.
• Organisational network methods.
• Data entry, data processing and management.
• Software options for visualisation and analysis.
• Network case study: Network research design.
Day 2: Key concepts and descriptive SNA
• Density and reciprocity.
• Clustering and triadic closure.
• Influential nodes and degree distributions.
• Preferential attachment and small worlds.
• Actor attributes.
• Multiplex and bipartite networks.
• Simple random graph distributions.
• Network case study: Why do we need statistical models for social networks?
Day 3: Introduction to ERGM
• What are exponential random graph models?
• Formation of network structure.
• MPNet software for network models.
• Working with graph distributions.
• Dependence assumptions: Bernoulli, Markov and Social Circuit models.
• Estimating ERGMs (modelling network data).
• Network case study: ERGMs for organisations.
Day 4: ERGM extensions
• Simulation and Goodness of Fit with ERGM.
• Estimating directed ERGMs.
• Estimating ERGMs with actor attributes.
• Multilevel networks.
• Causality in networks.
• ALAAMs – social influence models.
• Problem solving for ERGM model fit.
• Future directions.
• Network case study: Applications of ERGM.