IS 622: Network Science

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.

Learning outcomes
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.

Necessary prerequisites

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

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS7 SWS
Exercise class2 SWS6 SWS
ECTS credits6
Graded yes
Form of assessmentWritten exam (80 min)
Restricted admissionyes
Further information
Performing lecturer
Prof. Dr. Markus Strohmaier
Prof. Dr. Markus Strohmaier, Marlene Lutz
Frequency of offeringSpring semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., MMDS
Preliminary course workStudents must pass at least 50% of the written assignments in the exercise class in order to take the final exam.
LiteratureDavid Easley and Jon Kleinberg's Networks, Crowds, and Markets.
Laszlo Barabasi's Network Science
Aaron Clauset's course notes on Network Analysis and Modeling
Additional References
Stanley Wasserman and Katherine Faust's Social Network Analysis: Methods and Applications
Mark Newman's Networks: An Introduction
Course outlineThe lecture is divided into an introduction to basic concepts of networks, models of networks, mesoscopic structures, temporal networks and network dynamics.