Advanced Social Network Analysis
Overview
This unit aims to develop students’ capabilities for more in-depth and advanced statistical analysis of social network data. Coming to the unit with a strong understanding of the theoretical foundations of social network analysis, students will work to analyze the impact of social relationships in multiplex and multilevel ways. With a focus on applied data in businesses, organizations and communities, students will design and conduct social network research in an applied way. Students will move through a range of the most cutting-edge statistical models for social networks and engage in hands-on applied exercises with software such as MPNet and XPNet with real-world datasets and case studies. Through engagement with advanced models for network structure (i.e., social selection) and also node-level attributes (i.e., social influence), students will gain deeper insights into network processes to improve business, organisational and community outcomes.
Requisites
Rule
• You have completed INF80055 Social Network Analysis, or an equivalent
Assumed Knowledge
• You have a solid knowledge of statistics, in particular linear regression
• You are familiar with and have used Exponential Random Graph Models (ERGMs)
15-June-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Demonstrate advanced knowledge of cutting-edge social network methods, related theoretical concepts and their applications
- Critically analyze and synthesize network data using advanced statistical network models
- Apply knowledge of cutting-edge networks methods to design appropriate network research questions
- Critically evaluate and analyse complex network data and present an insightful and concise report as a response to an identified research question
- Apply, knowledge and skills in effective collaboration across a range of complex activities and contexts during data analysis
Teaching methods
| Type | Hours per week | Number of weeks | Total |
|---|---|---|---|
Online (asynchronous lecture) |
2 | 6 | 12 |
| Online (asynchronous lab exercises) | 2 | 6 | 12 |
| Online (Canvas Collaboration) | 2 | 6 | 12 |
| Unspecified Activities | 19 | 6 | 114 |
| Total | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment 1 | Individual | Pass Only | 1,2,3 |
| Assignment 2 | Individual | Pass Only | 1,2,3,4 |
| Presentation | Group | Pass Only | 1,2,3,4,5 |
| Report | Group | Pass Only | 1,2,3,4,5 |
Content
- Introduction to SNA
- ERGM for one-mode networks
- ERGM with actor attributes
- Simulation and estimation algorithms
- ERGM for bipartite networks
- ERGM for multiplex networks
- ERGM for snowball sampled networks.
- ERGM for multilevel networks
- ALAAM for multilevel networks
- Complex networks in action: Case Studies
- Assess key differences between analyses using different advanced SNA methods
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.