Social Network Analysis - Methods, Concepts and Applications
Overview and learning objectives
More than 300 years before Christ, the Greek philosopher Aristotle characterised man as a "zoon politikon " - a being with the elementary need to seek community and form communities. Today, in the age of the internet and digitalisation, this human characteristic can probably be illustrated most impressively using the example of online social networks such as Facebook, LinkedIn or Google+. Facebook alone now has well over one billion monthly users who network and exchange information online. Social networks - whether online or offline - are an integral part of our private and business lives and have a significant influence on what we know, what we buy, who we vote for and how successful we are at work. The constitutive feature of social networks is the relationships between members and the resulting network structure. The networking of members - i.e. their structural integration into the network - is central with regard to their interaction and communication possibilities and harbours valuable information for a wide range of business applications. For example, the identification of particularly well-connected members, so-called "key users" or "influential users", is of great importance in (viral) marketing and in the internal knowledge management of companies in order to reach the largest possible target audiences quickly and successfully place information in the network.
In the module "Social Network Analysis - Methods, Concepts and Applications", central concepts, methods and tools for recording and analysing social networks are covered and illustrated using practical examples and real data sets.
Students who have successfully completed this module are familiar with the fundamental theoretical principles and methods of social network analysis. In addition, they will be able to successfully solve practical problems with the help of the course content, interpret the results and derive recommendations for action.
Lecturers
Content information
Students who have successfully completed this module,
- can model social networks and are familiar with the necessary theoretical foundations,
- understand key characteristics (e.g. scale-free networks) and phenomena (e.g. small-world phenomenon) of social networks and can explain them,
- can evaluate and apply various methods for identifying central members in social networks and use them for real-world problems,
- are familiar with models for diffusion (e.g. of information or epidemics) in social networks and can identify and critically discuss practical applications,
- know and understand central models for describing the growth of social networks,
- can analyse (real) data on social networks using social network analysis methods (e.g. centrality measures) (also with the aid of software tools), interpret the results and derive recommendations for action.
This module covers the following technical content:
- Modelling networks and theoretical principles
- Random networks and scale-free networks
- Small-world phenomenon
- Centrality and communities in networks
- Diffusion in networks (e.g. of information, innovations and epidemics)
- Growth models for networks
- Barabási, A.-L. (2015) Network Science, abrufbar unter barabasi.com/networksciencebook/
- Borgatti, S. P.; Everett, M. G.; Johnson, J. C. (2013) Analyzing Social Networks. SAGE Publications Limited, London.
- Granovetter, M. S. (1973) The Strength of Weak Ties. In: American Journal of Sociology 78 (6), S. 1360-1380.
- Landherr, A.; Friedl, B.; Heidemann, J. (2010) Eine kritische Analyse von Vernetzungsmaßen in sozialen Netzwerken. In: WIRTSCHAFTSINFORMATIK 52 (6), S. 367-382.
- Milgram, S. (1967) The small world problem. In: Psychology today 2 (1), S.60-67.
- Newman, M. E. J.; Girvan, M. (2004) Finding and evaluating community structure in networks. In: Physical Review E 69 (2), S. 026113:1-026113:15.
- Newman, M. E. J. (2010) Networks – An Introduction. Oxford University Press, Oxford.
- Travers, J.; Milgram, S. (1969) An Experimental Study of the Small-World-Problem. In: Sociometry 32 (4), S. 425-443.
- Wasserman, S.; Faust, K. (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge.
- Watts, D. J.; Strogatz, S. H. (1998) Collective dynamics of 'small-world' networks. In: Nature 393 (6684), S. 440-442.
Organisational information
Next event start date: WiSe 25/26
Place: H12 (Dienstag) & H14 (Donnerstag)
Note: In October, the events will take place on Thursdays in H16.
Times: Tuesday, 2:00 p.m. to 4:00 p.m. & Thursday, 12:00 p.m. to 2:00 p.m.
Start of lectures: The first lecture will take place on 14 October 2025 from 2:00 p.m. to 4:00 p.m. in Lecture Hall H12.
Lecture (2 hours per week) with practical (2 hours per week)
Credit points are awarded on the basis of passing the written module examination. Registration for this examination does not require any prior proof of performance.
The module grade corresponds to the result of the module examination.
Main subjects: Schwerpunktfächer Technologie- und Prozessmanagement, Business Analytics, Wahlpflicht BWL.
Core areas: Digitalisierung & Data Science, Betriebswirtschaftlehre & Recht
degree programmes: M.Sc. Wirtschaftswissenschaften, M.Sc. Wirtschaftsinformatik - Digital Business und Analytics, M.Sc. Wirtschaftsmathematik, M.Sc. Wirtschaftschemie, M.Sc. Wirtschaftsphysik, M.Sc. Nachhaltige Unternehmensführung, M.Sc. Computational Science and Engineering und Studiengänge mit Nebenfach Wirtschaftswissenschaften