Skip Main Navigation
Page Content

Save This Event

Event Saved

Social Network Analysis 5-Day Course: Theory, Method and Application

Dr Peng Wang, Swinburne University of Technology

Monday, 12 February 2018 at 9:30 am - Friday, 16 February 2018 at 4:30 pm (AEDT)

Social Network Analysis 5-Day Course: Theory, Method...

Ticket Information

Ticket Type Sales End Price Fee Quantity
Standard 12/02/2018 $3,000.00 $0.00
PhD student (full time) 12/02/2018 $1,500.00 $0.00

Share Social Network Analysis 5-Day Course: Theory, Method and Application

Event Details

In this 5-day intensive course you will learn how to conduct social network research, moving from the fundamentals of networks to how to use cutting-edge statistical network models. Some general statistical knowledge is assumed (e.g., logistic regression). You will require your own PC laptop (or Mac with Windows installed – Mac OS is not supported). The course will cover the following key themes:

  • Statistical Inference with network data
  • Software for visualisation and analysis
  • Representing network data: from basics to advanced
  • Networks in action: Case studies
  • Applying SNA
  • One-on-one consultation times and group problem solving tasks

 

Day 1: Network Fundamentals
• What is distinctive about social network research?
• Network data: Representations and formats
• Qualitative versus quantitative data collection
• Primary versus secondary data sources
• Ethics for network research
• Organisational network methods
• Data entry, data processing and management
• Group allocation and briefing

 

Day 2: Key Concepts & Descriptive SNA

• Social Selection vs Social Influence
• The building blocks of networks: density; reciprocity; degree; connectivity; centrality; clustering; and preferential attachment (popularity)
• Multiplex and bipartite networks
• Introductory approaches to statistical inference

 

Day 3: Introduction to ERGMs
• What are exponential random graph models (ERGMs)?
• Formation of network structure
• MPNet software for network models
• Working with graph distributions
• Network dependence & emergence
• Estimating ERGMs (modelling network data)

 

Day 4: ERGM extensions
• Simulation and Goodness of Fit with ERGM
• Estimating directed ERGMs
• Estimating ERGMs with actor attributes
• ALAAMs – social influence models
• Problem solving for model fit

 

Day 5: Network Evaluation & Presentations
• Network evaluation
• Network problem solving
• Group presentations

 

The course includes all course exercise materials, lunch and afternoon tea.
Lecturers: A/Prof Dean Lusher, Dr Peng Wang, Prof Garry Robins, Dr Colin Gallagher, Dr James Coutinho, Dr Alex Stivala and Dr Maedeh Aboutelabi Karkavandi


Cost: $3,000 (Full-time PhD students $1,500; discounts for Swinburne staff and students who should contact Dr Peng Wang about payment by internal transfer).

Enquiries: Dr Peng Wang, Centre for Transformative Innovation: pengwang@swin.edu.au

 

Have questions about Social Network Analysis 5-Day Course: Theory, Method and Application? Contact Dr Peng Wang, Swinburne University of Technology

Save This Event

Event Saved

When & Where


Swinburne University of Technology
Wakefield Street
AGSE Building
Hawthorn, VIC 3122
Australia

Monday, 12 February 2018 at 9:30 am - Friday, 16 February 2018 at 4:30 pm (AEDT)


  Add to my calendar

Organiser

Dr Peng Wang, Swinburne University of Technology

Dr Peng Wang, Australian Research Council DECRA Fellow, is a computer scientist and network methodologist who has over 12 years’ experience in statistical models for networks. He is the creator of the PNet software for statistical models for social networks (Wang et al., 2009) which is one of the three most used software applications in the world for statistical network models.

  Contact the Organiser
Social Network Analysis 5-Day Course: Theory, Method and Application
Things to do in Hawthorn Class Other

Please log in or sign up

In order to purchase these tickets in installments, you'll need an Eventbrite account. Log in or sign up for a free account to continue.