Today, most complex systems have an underlying network structure. Examples of such networked systems include social networks, power grids, financial/economic networks, transportation systems, or computer networks. This course introduces students to the (data) analysis of complex networked systems. In particular, the Digital Network Analytics (DNA) elective teaches methods and tools to understand and answer questions such as:
- What does the structure of a network tell us?
- Who/what is important in a network, and why?
- How do epidemics, information, or changes spread?
- How robust is a network against attacks?
- How much cargo, information, or energy can be shipped from A to B?
Aside from the theoretical background, students will learn how to use software tools in order to apply their knowledge to the analysis of different types of real-world networks (e.g. social networks, transportation networks, computer networks, financial networks, or power grids) .
After completing the course, students understand the definition and the purpose of different network measures, to answer questions such as:
- What does the structure of a network tell us?
- Who/what is important in a network, and why?
- How do epidemics, information, or changes spread?
- How robust is a network against attacks?
- How much cargo, information, or energy can be shipped from A to B?
Moreover, they will be able to apply network analysis tools and interpret different measures that the tools provide.
According to the examination regulation, 80% attendance is required for a PI. This means that absence in one unit is tolerated. Beyond that, an exceptional reason must be given in accordance with WU's examination regulation. Any absence must be notified by email to the contact address before the start of the course.
The course is delivered using a combination of the following:
- presentation of basics by facilitators;
- demonstration of software tools by facilitators;
- practical exercises by students (including review sessions);
- presentation of mandatory specialist literature by students;
Digital Network Analytics 2 requires prior knowledge of the topics discussed in Digital Network Analytics 1.
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