Network science (2025/2026: Periode 4)

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Cursusdoel

After completing this course, the student:
1. has knowledge of important mathematical models for networks and their dynamics
2. can apply these models to important challenges in network science
3. has knowledge of important algorithms in network science
4. is able to find literature on algorithms in network science
5. is able to conduct an experimental study on algorithms in network science and present findings
6. is able to critique and review an experimental study of fellow students and incorporate feedback

Assessment
Exam (50%; goals 1, 2)
Term paper (40%; goals 1, 2, 3, 4, 5, 6)
Peer review (10%; goals 3, 6).

To pass the class, you need to submit a term paper, both for peer review and for final grading, participate in the peer review, and attend (most of) the guest lectures; otherwise, your grade is an NVD. If you obtain a grade for the exam that is strictly larger than 4 and a grade for the term paper that is strictly larger than 4, then your grade will be determined according to the weighting scheme above. Else, if your mark for the exam or the term paper is strictly larger than 4 (but not both), then your grade will be AANV.

You qualify for the repair if you grade was an AANV or your grade is insufficient (smaller than 6) but at least 4. The repair consists of a re-exam if your exam mark was at most 4; an update to your term paper if your term paper mark was at most 4; or your choice of one of these two options otherwise.

Vakinhoudelijk

Network science is an exciting new field that studies large and complex networks, such as social, biological, and computer networks. We will study, formalize, and quantify (social) network phenomena such as "the small world", "rich get richer", "birds of a feather flock together", "strength of weak ties" and others. The course will address topics from network structure and growth to the spread of epidemics. We also consider the diverse algorithmic techniques and mathematical models that are used to analyze such large networks, and give an in-depth description of the theoretical results that underlie them.

Topics include centralities, pagerank algorithms, assortativity, random graphs, configuration model, preferential attachment, network growth, robustness, giant components, power laws, diffusion, percolation, spreading phenomena, centralities, spectral clustering, and community detection.

Prerequisites
The course assumes that you have basic skills in algorithms and mathematics: familiarity with basic graph algorithms (graph search, shortest paths), such as offered in INFOAL Algoritmiek, and basic understanding of NP-completeness, such as offered in INFOAL or INFOMADS Algorithms for Decision Support. Having taken INFOAN Advanced Algorithms is not required. During the course, we also work with basic probabilities and some integrals.

Course form
Lectures, tutorials, term paper, peer feedback.

A major component of the course is for students to perform their own experimental study on algorithms for the community detection problem. The results of this study will be presented in the term paper. This term paper will be reviewed by your student peers.

Literature
Recommended:
- A. Barabasi, “Network Science”, for free online
- D. Easley, J. Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, for free online
- M.E.J. Newman, “Networks”, 2nd edition (2018)

The course is based on parts of all three books. The Newman book is the most comprehensive reference to the material of the course.

Werkvormen

Hoorcollege

Toetsing

Eindresultaat

Verplicht | Weging 100% | ECTS 7,5

Ingangseisen en voorkennis

Ingangseisen

Je moet een geldige toelatingsbeschikking hebben

Voorkennis

The course assumes that you have basic skills in algorithms and mathematics: familiarity with basic graph algorithms (shortest paths, flows), such as offered in INFOAL Algoritmiek, and basic understanding of NP-completeness, such as offered in INFOAL or INFOMADS Algorithms for Decision Support.<br> Having taken INFOAN Algorithms and Networks is helpful, but not required. During the class, we also work with basic probabilities and some integrals.

Voorkennis kan worden opgedaan met

Algoritmiek (INFOAL)<br> Algorithms for decision support (INFOMADS)<br> Advanced Algorithms (INFOAN)

Voertalen

  • Engels

Cursusmomenten

Gerelateerde studies

Tentamens

Er is geen tentamenrooster beschikbaar voor deze cursus

Verplicht materiaal

Er is geen informatie over de verplichte literatuur bekend


Aanbevolen materiaal

  • LIT LIJST
    • A. Barabasi, "Network Science", for free online<br> • D. Easley, J. Kleinberg, “ Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, for free online<br> • M.E.J. Newman, "Networks", 2nd edition (2018).<br><br> The class is based on parts of all three books. The Newman book is the most comprehensive reference to the material of the class.

Coördinator

dr. E.J. van Leeuwen e.j.vanleeuwen@uu.nl

Docenten

dr. I.R. Karnstedt-Hulpus i.r.karnstedt-hulpus@uu.nl
dr. E.J. van Leeuwen e.j.vanleeuwen@uu.nl
dr. J.M.M. van Rooij J.M.M.vanRooij@uu.nl

Inschrijving

Deze cursus is open voor bijvakkers. Controleer wel of er aanvullende ingangseisen gelden.

Inschrijving
Van maandag 26 januari 2026 tot en met vrijdag 6 februari 2026

Na-inschrijving
Van maandag 30 maart 2026 tot en met dinsdag 31 maart 2026

Inschrijving niet geopend
Permanente link naar de cursuspagina
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