Social network analysis

Module title:

Social network analysis

Module overview:

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 entrepreneurs.
In this module students will:
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.

Literature:

Required readings:
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


Supplementary readings
1. Knoke, D., Yang., S. (2008). Social Network Analysis, (2nd Ed), Newbury Park: Sage