Admission requirements
None.
Description
Transportation, traffic, energy, communication and social networks form the backbone of modern society. In recent years, there has been a growing fascination with the complex “connectedness” that such networks provide. This connectedness manifests itself in many ways: in the rapid growth of the Internet and the World Wide Web, in the ease with which global communication takes place, in the speed at which news and information travels around the world, and in the spread of an epidemics or a financial crisis. These phenomena are based on the links that connect people and their decisions, and have global consequences. The course aims to provide students with a concise introduction into this lively area, and covers both theoretical principles and practical applications from a variety of different directions. Complex Networks is a multi-disciplinary course: it exposes views on Complex Networks from Physics, Mathematics and Computer Science, is aimed at students of and is taught by faculty from these disciplines.
The course is open to third-year bachelor Mathematics, Physics and Astronomy students and first-year master students of all programs of Physics, Mathematics, Astronomy and Computer Science (including Bioinformatics, ICT&Business, Informatica&Economie).
The course assumes basic knowledge of physics, mathematics and computer science at the bachelor level. In particular, familiarity with elementary notions from calculus, probability theory and statistical physics are helpful. Some light computer programming will be involved as well.
Topics:
Real-world networks: concepts, properties, challenges;
Models: random graphs, preferential attachment, small worlds;
Maximum-entropy statistical ensembles of networks;
Network algorithms, visualization, simulation;
Percolation & epidemiology on networks;
Pattern detection on networks;
Computational complexity of processes on networks.
Course objectives
This course aims to provide an introduction to the field of complex networks, covering both theoretical principles and practical applications from different perspectives. Complex Networks is a multi-disciplinary course: it exposes views on the area from mathematics, physics and computer science, and is open to students from all programs in these three disciplines. At the same time it assumes basic knowledge at the bachelor level in each of these disciplines, including key concepts from calculus (differentiation, integration, limits), probability theory (probability distributions, random variables, stochastic processes), statistical physics (ensembles, entropy), and computer programming (C or Python). In terms of panorama, the course material is both challenging and rewarding. At the same time, it chooses a style of presentation that is tuned to a mixed audience, which inevitably means a breach with the styles that are commonly adopted in the three disciplines separately.
As part of the learning goals of the course, the students should develop an interdisciplinary view to the study of Complex Networks. This is the reason why the course is taught by three instructors with different background. Each lecture (and the associated homework) takes a viewpoint from either Mathematics, Physics, or Computer Science. To further strengthen interdisciplinarity, students are asked to form mixed teams for doing the homework together.
Schedule
The timetables are available through My Timetable (see the button in the upper right corner).
Teaching method
See Brightspace
Assesment method
The teachers will inform the students how the inspection of and follow-up discussion of the exams will take place.
The final grade will be determined as follows:
weekly assignments (30%)
Final exam (70%)
Resit, review & feedback
Examinations are held twice during the academic year for each component offered in that academic year. Midterm tests cannot be retaken. The Board of Examiners determines the manner of resit for practical assignments.
For review and feedback, see Brightspace.
Reading list
Course notes (provided on Brightspace)
Registration
Enrolment through MyStudyMap (button in upper right corner) is mandatory. General information about course and exam enrolment is available on the website.
Contact
For substantive questions, contact the lecturer(s) (listed in the right information bar).
Remarks
Transferable skills: scientific curiousity, creating connections among different contexts and disciplines, thinking out of the box, being able to formulate hypotheses about problems for which one has no prior knowledge, abstraction and generalization in a multidisciplinary context.
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.