Course description

Digital Transformation School – Applying Social Network Analysis – Summer Course


Digital technologies are increasingly permeating the way we work, live, and think. This summer school intends to equip students with a set of analysis techniques to understand the antecedents, effects and outcomes of this digital transformation better. The focus will be on social network analysis as a method to study the social and economic implications of digital technology from an empirical point of view. In particular, during two weeks, we encourage students to reflect on the impact of digital technologies on the way we work, may it be through participating in new modes of virtual work, may it be through new forms of crowdworked creativity, or participating in new forms of collaboration that combine elements of work and play. We will try to uncover and find managerial points of action for instance for the sharing economy, to the practice of influential marketing, to new forms of algorithmic management, to other new forms of working.

Our search for solutions will be underpinned by learning about social network methods. Social network analysis is interested in the relational properties of organizations and individuals. While the method has been developed in the pre-digital era for small-scale data, it is especially suited for user-generated trace data that contains relational elements. By using social network analysis, communities and sub-communities can be identified and clustered based on core attributes. Moreover, social network analysis is a key method to identify influentials or important and noteworthy elements in a network. Finally, new developments in social network analysis in recent years, such as exponential random graph models (ERGM), allow to make statistical inference with network data and test for the presence of structural effects such as homophily, reciprocity, and transitivity. Thus, social network analysis is a versatile and widely used method with many benefits, especially in times of big data and user-generated data from social and digital media. A solid foundation in social network theory and methods will provide the students not only with a toolset to analyze communities effectively but also with a relational way of thinking through core concepts of the method.  

Course content

  1. Introduction: Why Social Networks? 
    An overview of the social networks approach, and a showcase of current examples in the form of interesting research and company studies concerning challenges of digital transformation. Students will get a first grasp of practical questions and challenges related to new forms of production and work that come with digital technologies.
  2. Principles of Social Network Analysis I 
    The scientific origins of social network analysis, introducing some fundamental concepts from graph theory. Introduction of concepts such as ego-, group-, and global networks, and their applicability to real-world challenges.
  3. Principles of Social Network Analysis II 
    Core Concepts of Social Network Analysis will be introduced further, such as network structure, and network centrality.
  4. Practice-Oriented Social Network Analysis 
    Introduction to software such as pajek and gephi
  5. Essentials of Data Gathering 
    Sources for Gathering Social Network Data, from APIs to repositories to services. Introduction to working with SQL-Databases
  6. Visual and Qualitative Social Network Analysis 
    Qualitative Gathering and Analysis of relational Data, Network Visualization and Visual Analysis of Networks
  7. Advanced Quantitative Social Network Analysis 
    Regression and work with R
  8. Project Presentation and Discussions 

The students will present their findings to the group and discuss them critically with the other participants of the course. The main findings, implications and challenges during the research project are addressed, trying to condense the key learning across the groups.

Learning outcome knowledge

After taking the summer school, the students should have acquired knowledge of:

  • relational dynamics of social and digital media,
  • data sources that feed into the analysis of social networks,
  • network visualizations and key metrics that describe the structure of a social network

Exam organisation

  • Presentation: 30%
  • Written assignment: 70%