- Class 30
- Practice 30
- Independent work 120
Social network analysis
With ready access to computing power, the popularity of social networking websites such as Facebook, and automated data collection techniques the demand for solid expertise in SNA has recently exploded. In this module, students learn how to conduct SNA projects and how to approach SNA with theoretic, methodological, and computational rigor.
The objectives of this module are to enable students to:
• Formalize different types of entities and relationships as nodes and edges and represent this information as relational data
• Plan and execute network analytical computations
• Use advanced network analysis software to generate visualizations and perform empirical investigations of network data
• Interpret and synthesize the meaning of the results with respect to a question, goal, or task
• Collect network data in different ways and from different sources while adhering to legal standards and ethics standards.
In this module students will learn about social network analysis as a type of analysis that measure networks of people and helps analysts determine how nodes (people) are connected and around what issues and key facts. With social network analysis, students can take a snapshot of the network and figure out both the network strengths and weaknesses, and use that to grow a better and more robust network for a greater and more dramatic impact, as part of data science process.
It is important for students to take this module in order to develop a knowledge and understanding of set of social network analysis concepts and metrics to systematically study these dynamic processes. Analysts in information visualization can also benefit by discovering patterns, trends, clusters, gaps, and outliers, even in complex social networks. Each day solutions for better network insights are being found that bring competitive advantages to business product developers, opportunities for government agency staffers, and new possibilities for nongovernmental social entrepreneur.
1. Hanneman, R.A., Riddle, M. (2005) Introduction to Social Network Methods, Riverside: University of California
2. Scott, J. (2007). Social network analysis: A handbook (2nd Ed.), Newbury Park: Sage
1. Knoke, D., Yang., S. (2008). Social Network Analysis, (2nd Ed), Newbury Park: Sage
Minimal learning outcomes
- Explain basic principles of network formation.
- Identify possible connections of several types of networks that occur in nature
- Identify measures of centrality in network analysis.
- Define scenario to be solved by social CRM and SNA techniques
- Select social network analysis functionality in context of analysis of complex network with multiple entities.
- Describe steps in data collection, preparation and processing using social media profiles and social media analysis software.
- Identify most common functionalities of social network analysis software.
Preferred learning outcomes
- Explain the basic principles of network formation.
- Rank according to the complexity of the distribution of connections of several types of networks that occur in nature.
- Choose measures of centrality in network analysis.
- Explain the stages of planning and implementation of social CRM in a business entity.
- Describe social network analysis functionalities to analyze a more complex network with multiple entities.
- Apply methods of data collection, preparation and processing using social media profiles and social media analysis software.
- Select specific functionalities of social network analysis software.