Applying Social Network Analysis
- Master Level - 6 ECTS credits
- A variety of social and cultural activities included
- Eligible applicants must have a completed degree comparable to a bachelor’s degree. 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.
- Two-week course from 25 June to 6 July
- Welcome information 23 June
Included in the programme
- Lectures in Digital Transformation – Applying Social Network Analysis by highly qualified faculty members from BI Norwegian Business School
- Social and cultural activities – including a weekend expedition, sightseeing and outdoors activities (hiking, climbing, barbecues)
- Insight into Norwegian industries through guest lectures and company visits
About the course
Digital technologies are increasingly permeating the way we work, live, and think. This summer school course 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.
After taking this summer school course, students will
- understand digital transformation better
- be equipped with the concepts to analyse relational dynamics of social and digital media effectively
- have the methodological tools at hand to analyse social networks from a range of data
- be able to interpret network visualisations and articles using social network analysis appropriately
- Practical skills to collect social media data – APIs, SQL, etc.
- Ability to conduct social network analysis on a range of data using software such as Gephi, Netlytic and R
- Network visualisation skills using Gephi and other software (e.g., UCINET Netdraw)
- Have a first understanding of complex network modelling methods (e.g., ERGM, Siena)
- Developing a critical understanding of the managerial and social challenges of digital transformation
- Understanding the relational nature of society
- Interpreting the dynamic role of influence in social networks as expressed in phenomena like influentials and opinion leaders
- Reflecting on the role of data, especially big data, and its role in transforming work and society
- Integrating key concepts learned in the course such as social capital and social networks
The following topics will be covered during the two weeks:
- 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.
- 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.
- Principles of Social Network Analysis II
Core Concepts of Social Network Analysis will be introduced further, such as network structure, and network centrality.
- Practice-Oriented Social Network Analysis
Introduction to software such as pajek and gephi
- Essentials of Data Gathering
Sources for Gathering Social Network Data, from APIs to repositories to services. Introduction to working with SQL-Databases
- Visual and Qualitative Social Network Analysis
Qualitative Gathering and Analysis of relational Data, Network Visualisation and Visual Analysis of Networks
- Advanced Quantitative Social Network Analysis
Regression and work with R
- Project Presentation and Discussions
Students will present their findings to the group and discuss them critically with the other participants in the course. The main findings, implications and challenges during the research project are addressed, trying to condense the key learning across the groups.