Modeling and Data Analysis in Complex Networks

Big Data is mostly obtained from features of components and the interactions between components in large complex systems. Examples are (1) end user features and interactions in both online and real-world social networks like Twitter, LinkedIn (2) data from content sharing platforms such as YouTube (3) physiological data of the brain and (4) stock prices etc. in economic systems. Such a dataset is networked in nature i.e. the data of the system components or interactions are (cor)related to each other.

This course introduces the basic methodologies to analyze, model, interpret and predict such Networked Data that enable us to further intervene or optimise the system, combining advances from network science, modeling of dynamic processes and statistical physics, beyond machine learning algorithms. These methods will be applied to diverse real-world datasets obtained from e.g. Facebook, LinkedIn, YouTube, the brain etc.

The final grade of the course consists of the following components:

• Assignments (weighting 20%)

• Final project (weighting 80%)

Final grade calculation = 0.2 * Assignments + 0.8 * Final project

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Assignments: re-submit deliverable

• Final project: re-submit deliverable

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).

In case the assignment or project is not submitted on time, which means it won't be evaluated, there won't be any repair opportunity.