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Introduction to Graph Theory
Last Updated: 2026-06-03 00:14:12
Abstract
Basic notions, trees, spanning trees, Caley's formula, vertex and edge connectivity, 2-connectivity, Mader's theorem, Menger's theorem, Eulerian graphs, Hamilton cycles, Dirac's theorem, matchings, theorems of Hall, König and Tutte, planar graphs, Euler's formula, basic non-planar graphs, graph colorings, greedy colorings, Brooks' theorem, 5-colorings of planar graphs
Objective
The students will get an overview over the most fundamental questions concerning graph theory. We expect them to understand the proof techniques and to use them autonomously on related problems.
Resources
Lecture Notes
Lecture will be only at the blackboard.
Literature
West, D.: "Introduction to Graph Theory" Diestel, R.: "Graph Theory" Further literature links will be provided in the lecture.
Learning Materials (Links)
- Moodle course
- Moodle-Kurs / Moodle course
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 180 minutes
- Aids
- Students are allowed to bring ONLY a printed copy of the lecture notes with no extra writing (highlighting and blank post-its are allowed).
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Introduction to Graph Theory |
|
28 h semesterly |
| exercise | Introduction to Graph Theory |
|
7 h semesterly |
Offered In
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Computational Biology and Bioinformatics Master (More informations at: )
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Advanced Courses (A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and 10 ECTS in the Biology category.)
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Theory (At least 16 ECTS need to be acquired in this category.)
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Track: Signal Processing and Machine Learning (The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
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Specialization Courses (These specialization courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. Semester / Research Projects are not allowed in this category. A minimum of 40 credits must be obtained from specialization courses during the MSc EEIT.)
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