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No. Type(s) Class Title
5892 PI Digital Network Analytics II Präsenz-Modus
This class is only offered in summer semesters.
Registration via LPIS
from 2024-02-12 15:00 to 2024-02-15 23:59
De-registration via LPIS
from 2024-02-12 15:00 to 2024-05-05 23:59

Instructor(s) Assoz.Prof PD Dr. Mark Strembeck, Assoz.Prof PD Dr. Stefan Sobernig
Subject(s) Master Programs Digital Network Analytics II
Credit hours 2
Language of instruction English

Detailed schedule
Wed, 2024-05-08 10:00-14:00 D2.0.038 (Map)
Wed, 2024-05-15 14:00-18:00 D2.0.038 (Map)
Wed, 2024-05-22 10:00-14:00 D2.0.038 (Map)
Wed, 2024-05-29 10:00-14:00 D2.0.038 (Map)
Wed, 2024-06-05 10:00-14:00 D2.0.038 (Map)
Wed, 2024-06-12 10:00-14:00 D2.0.038 (Map)
Wed, 2024-06-19 10:00-15:00 TC.3.07 (Map)
Wed, 2024-06-26 10:00-14:00 P TC.3.05 (Map)
Download schedule (ical) | Subscribe schedule

Further information https://learn.wu.ac.at/vvz/24s/5892

Contact details:
dna@complex.wu.ac.at
Contents:

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) .
Learning Outcomes:

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.
 
Attendance requirements:

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.
Teaching/learning method(s):

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;
Assessment:
  • Homework assignment (60%)
  • Written exam (40%)
Prerequisites:

Digital Network Analytics 2 requires prior knowledge of the topics discussed in Digital Network Analytics 1.
Last edited: 2024-02-20 11:00
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