Network Science

IS 622 for Master students (M.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., MMDS)

LecturerProf. Dr. Markus Strohmaier
Course FormatLecture
OfferingFSS
Credit Points6 ECTS
LanguageEnglish
GradingWritten exam
Exam DateTo be announced
Information for StudentsThe course is limited to 60 participants. Please register in time via Portal2. Students must pass at least 50% of the written assignments in the exercise class in order to take the final exam.
Marlene Lutz, M.Sc.

Marlene Lutz, M.Sc.

For further information please contact Marlene Lutz.

Course Information

  • Course Description

    The lecture gives an introduction to the analysis of networks. It includes theoretical foundations of social networks (definitions, representation as a graph, local structures), elementary graph algorithms (shortest path, clustering coefficient, ...), centrality measures for social networks (PageRank, betweenness centrality, ...), methods for community detection, phenomena in empirical social networks (scale-free networks, small-world phenomenon, homophilia, ...), graph models (random graphs, preferential attachment,...), robustness of graphs, as well as dynamics in networks, epidemics and information cascades.

  • Objectives

    Knowledge: Upon successful completion of this module, students will have developed an understanding of basic concepts and algorithms for analyzing networks and have acquired knowledge of empirically occurring phenomena in networks. Furthermore, the students get an overview of current analysis tools of
    social networks. 

    Skills: The students learn how to analyze empirical social networks with regard to their structure and mathematical properties such as the determination of central nodes, as well as methods to understand dynamics in social networks. In addition, the students learn how to use the most common program libraries for analyzing social networks. 

    Competences: The students should be able to effectively use analysis methods for social networks in other areas of application.

  • Requirements

    Required: -

    Recommended: Basic knowledge of algorithms and data structures as well as programming concepts and methods, practical programming skills (Python), basic knowledge of statistics