QTEM summer school

Digital Methods and Transformation - Applied Social Network Analysis.


24 Jun 2019 - 5 Jul 2019

Oslo (Norway)


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 outline

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 process and requirements to students Students are expected to have taken classes in statistics and have working knowledge of MS Excel and SPSS. We expect students to have a solid grasp of the English language as well as a strong interest in the issues at hand, and to actively participate in class. Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on the course homepage/its learning or text book. This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts. At resit, all exam components must, as a main rule, be retaken during next scheduled course. Computer-based tools: Gephi, Netlytic, Sentistrength, R Software tools R Qualifications All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Other information


ECTS equivalent


Local credits

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