Introduction to Social Network Analysis using Advanced Data Mining

 

Content

Social networks play an ever increasing role in our society. Facebook, Instagram, and Twitter are just some examples of internet sites where users can network. Many traditional business decisions will be influenced by social network analysis (SNA). Loan granting or marketing campaigns are just two examples. But also less traditional areas, such as e.g. investigation of organized crime, can benefit from this relatively new approach. This course first lays the foundation for social network analysis by introducing advanced data mining techniques. Then the main topics related to SNA will be introduced. Applications with real-world data from social networks using the respective software tools will conclude the course.

Teaching Objectives

This course seeks to enhance participants’ ability to:

  • understand the potential of social network analysis (SNA) in different areas,
  • select the adequate methods for network analysis,
  • analyze social networks using advanced data mining techniques,
  • propose decisions based on the respective network analyses.

Prerequisites

  • Solid command of English.
  • Willingness to engage in preparatory readings of case studies and/or research papers.
  • Exchange and Erasmus students are cordially invited to apply for participation in this course

Due to the interactive teaching format, the number of participants is limited to 30.

Grading

The final grade will be composed as follows:

  • Group work including student presentation and report (weight: 50%) and
  • Individual written exam (60 minutes) (weight: 50%).

Recognition

  • For students of the Master's degree in Business Administration (RWTH) and Master's degree in Economics (RWTH), this course can be credited in all specializations.
 

Downloads