270193 VU (Introduction to) Network analysis with Python (2025S)
Prüfungsimmanente Lehrveranstaltung
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An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Di 04.02.2025 08:00 bis Di 25.02.2025 23:59
- Abmeldung bis Di 25.02.2025 23:59
Details
max. 12 Teilnehmer*innen
Sprache: Deutsch, Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Donnerstag 06.03. 10:00 - 16:00 Seminarraum Physik Sensengasse 8 EG
- Donnerstag 13.03. 10:00 - 16:00 Seminarraum Physik Sensengasse 8 EG
- Donnerstag 20.03. 10:00 - 16:00 Seminarraum Physik Sensengasse 8 EG
- Donnerstag 27.03. 10:00 - 16:00 Seminarraum Physik Sensengasse 8 EG
- Donnerstag 03.04. 10:00 - 16:00 Seminarraum Physik Sensengasse 8 EG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Assessments• Python programming assignment
• Participation in student discussions
• Oral presentation of a research paper
• Short report on a programming assignment
• Participation in student discussions
• Oral presentation of a research paper
• Short report on a programming assignment
Mindestanforderungen und Beurteilungsmaßstab
The course is designed to be an introduction to network science and to programming. Students are not expected to have prior programming experience. Course attendance is mandatory. Students may be excused for one day unless otherwise agreed upon. 50% of the maximal points, as outlined below, must be attained to pass the course.Marking SchemeA maximum of 100 points can be achieved in the course. The 100 points are divided into:
• Programming assignment: 45 points
• Research paper presentation: 15 points
• Discussions (graded by participation): 10 points
• Written report: 30 pointsGrades will be assigned as follows:• 1 (excellent): 100-89 points
• 2 (good): 88-76 points
• 3 (satisfactory): 75-63 points
• 4 (sufficient): 62-50 points
• 5 (insufficient): 49-0 points
• Programming assignment: 45 points
• Research paper presentation: 15 points
• Discussions (graded by participation): 10 points
• Written report: 30 pointsGrades will be assigned as follows:• 1 (excellent): 100-89 points
• 2 (good): 88-76 points
• 3 (satisfactory): 75-63 points
• 4 (sufficient): 62-50 points
• 5 (insufficient): 49-0 points
Prüfungsstoff
There are no restrictions with regards to the resources that students can use to complete their assignment(s), provided that all work submitted/ presented for assessment is original and has not been plagiarised. The use of LLMs and generative AI is permitted, but their use must be declared in the respective work. All assessments are based on the materials provided during the course.
The additional reading materials may be used to gain a deeper understanding of the content provided in the lectures and practicals. Student may be expected to read some of the additional material(s) in order to gain full marks.
The additional reading materials may be used to gain a deeper understanding of the content provided in the lectures and practicals. Student may be expected to read some of the additional material(s) in order to gain full marks.
Literatur
Reading MaterialsA majority of the course material is based on the following textbook and chapters:Barabasi, A.-L. & Marton, P. (2016) Network Science. Cambridge University Press. [available online at http://networksciencebook.com/]
• Chapter 1 -5, 8-9The course is supplemented with examples from the following research papers and others:Barabási, A., Oltvai, Z. (2004) Network biology: understanding the cell’s functional organization. Nature Reviews Genetics. 5, 101–113.Barabási, A.-L., Gulbahce, N. & Loscalzo, J. (2011) Network Medicine: A Network-based Approach to Human Disease. Nature Review Genetics. 12,56-68.Compeau, P.E.C., Pevzner, P.A. & Tesler, G. (2011) Why are de Bruijn graphs useful for genome assembly? Nature Biotechnology. 29, 987-991.Firth, J.A., Hellewell, J., Klepac, P., Kissler, S., CMMID COVID-19 Working Group, Kurcharski, A.J., Spurgin, L.G. (2020) Using a real-world network to model localized COVID-19 control strategies. Nature Medicine. 26, 1616-1622.Motter, A.E., Gulbahce, N., Almaas, E. & Barabási, A.-L. (2008) Predicting synthetic rescues in metabolic networks. Molecular Systems Biology. 4, 168.Orth, J., Thiele, I. & Palsson, B. (2010) What is flux balance analysis? Nature Biotechnology. 28, 245–248.Jeong, H., Mason, S.P., Barabási, A.-L. & Oltvai, Z.N. (2001) Lethality and centrality in protein networks. Nature, 411: 41-42.Watts, D.J. & Strogatz, S.H. (1998) Collective dynamics of ‘small-world’ networks. Nature. 393, 440-442.
• Chapter 1 -5, 8-9The course is supplemented with examples from the following research papers and others:Barabási, A., Oltvai, Z. (2004) Network biology: understanding the cell’s functional organization. Nature Reviews Genetics. 5, 101–113.Barabási, A.-L., Gulbahce, N. & Loscalzo, J. (2011) Network Medicine: A Network-based Approach to Human Disease. Nature Review Genetics. 12,56-68.Compeau, P.E.C., Pevzner, P.A. & Tesler, G. (2011) Why are de Bruijn graphs useful for genome assembly? Nature Biotechnology. 29, 987-991.Firth, J.A., Hellewell, J., Klepac, P., Kissler, S., CMMID COVID-19 Working Group, Kurcharski, A.J., Spurgin, L.G. (2020) Using a real-world network to model localized COVID-19 control strategies. Nature Medicine. 26, 1616-1622.Motter, A.E., Gulbahce, N., Almaas, E. & Barabási, A.-L. (2008) Predicting synthetic rescues in metabolic networks. Molecular Systems Biology. 4, 168.Orth, J., Thiele, I. & Palsson, B. (2010) What is flux balance analysis? Nature Biotechnology. 28, 245–248.Jeong, H., Mason, S.P., Barabási, A.-L. & Oltvai, Z.N. (2001) Lethality and centrality in protein networks. Nature, 411: 41-42.Watts, D.J. & Strogatz, S.H. (1998) Collective dynamics of ‘small-world’ networks. Nature. 393, 440-442.
Zuordnung im Vorlesungsverzeichnis
BC-CHE II-8, CH-CBS-05, Design
Letzte Änderung: Mi 07.05.2025 00:02
Students will know different types of networks and will be able to translate various (chemical) datasets into suitable network representations. Students will gain an understanding of several network properties and modeling techniques and will be able to evaluate their use and their outcomes to answer scientific research questions.
Furthermore, students will gain a gentle introduction to programming in Python in order to learn how to use the Networkx package for generating and analysing networks.Course Content• Introduction to graph theory and the calculation of structural network properties
• Discussion of different network types and examples of real networks
• Introduction to analysing networks in Python using the networkx package
• Discussion of good coding practices and writing pseudo code
• Analysis of a real network and discussion of outcomesMethodThe course will be divided into lectures and programming practicals. Each set of theory will be followed by examples on how to implement the theory in Python and a set of programming tasks. A combination of course participation, programming exercises, presentations and a short written report will be used to assess the material covered in the lectures and practicals.To complete the practical tasks of this course, students will require access to a computer or laptop with a web browser. Programming tasks are designed to be hosted on a server which has the required software pre-installed and to which students enrolled in the course will be granted access.The course will be instructed in English.