Course: Social Networks
RWTH Aachen, Summer Semester 2017-2018
Classes: TBA, Room TBA
TBA RWTH webpage course link
Markus Strohmaier, María Pereda
Questions related to this course:
Your question might be of interest to other students! Therefore, before sending an e-mail to the instructor or the teaching assistants, please consider posting it to the course newsgroup TBA. The course team reads the newsgroup frequently and will try to answer your question as soon as possible.
Students with special needs:
If you need accommodation for any type of physical or learning disability, please contact me via e-mail to set up a meeting where we can discuss potential modifications for your participation.
Classes will start on Monday Mar 4th 2013. Students are required to enroll via TUGonline.
About the course:
This module gives an overview on the analysis of social networks. It includes:
- Foundations of social networks (defintions, network representation, local structures).
- Basic network algorithms (shortest path, clustering coefficient, ...).
- Centrality measures for social networks (PageRank, betweenness centrality, ...).
- Emerging phenomena in empirical social networks (powerlaw, small world, Dunbar’s number…).
- Network models (Random graphs, preferential attachment, exponential random graphs, ...).
- Dynamics on networks, epidemics, and information cascades.
On successful completion of this module, students should know about fundamental concepts and algorithms of network analysis and should have learned about emerging phenomena in empirical networks. Students should also obtain an overview on state-of-the-art analysis tools for social networks.
- analyze empirical social networks with respect to their structure, compute network properties, identify central nodes, and investigate network dynamics
- apply state-of-the libraries for analyzing social networks.
Students establish critical thinking regarding assumptions and possibilities of social network analysis
- Basic programming skills as taught in "Programmierkurs (Java)" and “Scientific Programming in Python"
- Basic knowledge about statistics
- "Datenstrukturen und Algorithmen" and "Datenbanken und Informationssysteme" or equivalent
A. Barabasi: "Network Science", 2016
M. Newman: "Networks: An Introduction", 2009
D. Easley and J. Kleinberg: "Networks, Crowds, and Markets: Reasoning About a Highly Connected World", 2010
Benotung: The grade is mainly determined by a written exam at the end of the course. Up to 25% of the grade may be determined by solving tasks at home. For low numbers of participants, the written exam can be replaced by an oral examination.
Umfang SWS Vorlesung: 4 ECTS (3 SWS Kontaktzeit + 75h Selbststudium)
Umfang SWS Übung: 2 ECTS (2 SWS Kontaktzeit + 30h Selbststudium)
Umfang ECTS: 6 ECTS total
Course work will consist of
Home assignments: over the duration of the course, will be graded
1 final exam: at the end of the course, will be graded
The following weights will be assigned to home assignments and the final exam (totalling 100%):
Home Assignments: 50% Total (consists of HA1: 20%, HA2: 10%, HA3: 10%, HA4:10%)
Final Exam: 50%
In order to obtain a positive grade, you need to have a total score of 51% or more. Please also see section "Course Policies" below.
Preliminary course schedule and weekly readings:
Note to students: Changes to this schedule will likely be made. Additional readings may be assigned. Access credentials for protected resources will be handed out in class.