Introduction to Social Network Analysis - online

To prevent obesity or smoking initiation among teenagers, who should be targeted in an intervention? How can we contain the spread of an infectious disease under limited resources? Who should be vaccinated first in order to be most effective during vaccination shortages? How can we dismantle a terrorist organization, a drug distribution network or disrupt the communication flow of a criminal gang?

Social network analysis offers the theoretical framework and the appropriate methodology to answer questions like these by focusing on the relationships between and among social entities. Unlike transitional research methods, we shift the object of study from the individual as the unit of analysis, to the social relations that connect these individuals. A network is therefore a structure composed of units and the relationships that connect them. Network analysis is about the position of these units, the overall structure and how these affect the flow of information.

The focus of this two day online course is not so much on how to express these concepts formally through mathematics, but rather on how to use appropriate software to acquire measurements for these concepts in the data and use them rigorously in empirical hypothesis testing. The majority of the course will focus on descriptive methods of network analysis, but we will also discuss network-specific models and inferential methods for network analysis.

The course covers:

• Foundations social networks data: relational structures and data collection;
• Manipulation of network data (matrix algebra and graph theory);
• Node-level measurements;
• Graph-level measurements;
• Network visualization.

Learning outcomes:

• Navigate the key areas of research in social networks;
• Acquire knowledge of data collection and suitable data structures for analysing social networks;
• Develop an understanding of social phenomena through the lenses of the social networks theory;
• Learn how to operate software package for the analysis of social network data;
• Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;
• Acquire the ability and comprehension to independently read scientific literature using social network analysis methodology;
• Learn the fundamentals of social network analysis to acquire the necessary proficiency to explore more advanced topics autonomously.

Basic knowledge of Excel and data matrices will be required.

This course will run from 9.00am - 5.30pm each day.

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