Facts about the course

ECTS Credits:
2.5
Responsible department:
Faculty of Logistics
Course Leader:
Falko Müller
Lecture Semester:
Autumn
Teaching language:
English
Duration:
½ year

LOG904-170 Complex network analysis with R - an introduction to R and its application to the study of complex networks (Autumn 2020)

About the course

Supply chains, social media interactions, neural networks, global transportation and mobility structures - networks are all around us. Studying their fundamental properties enables us to understand the nature of such complex systems, to assess the role and the interactions of their stakeholders, and to make predictions on likely future dynamics within them. This seminar introduces students to major concepts of complex network research and teaches them how to examine networks with the R software environment.

The seminar starts with a brief introduction of R and its basic functionalities necessary to analyze networks. Next, we discuss the formal representation of different network types and introduce several global and local network metrics from a theoretical perspective, as well as, from their specific implementation in R. We analyze different network (growth) models and discuss how they determine the properties of the network. The seminar ends with the presentation of a few advanced concepts in complex network research.

The seminar is designed for the interested SCM- or Transp. student. No prior knowledge of R is required. However, the successful course participant should feel comfortable working with code-based software.

Keywords: complex network research, R - software environment, unweighted and weighted networks, standard network metrics, random graphs, small-world property, preferential attachment, normalization, communities

The course is connected to the following study programs

Recommended requirements

None

Forms of teaching and learning

Three hours of lectures in the morning are used to discuss concepts and to present their implementation/analysis in R. In the afternoon, the students are required to work on exercises/assignments in the computer lab and under the supervision of the lecturer.

Examination

Form of assessment: Home assessment without presentation 1

  • Proportion: 25%

  • Duration: -

  • Grouping: Individual

  • Grading scale: Letter (A - F)

  • Deadline for submission: Tuesday 09:00


Form of assessment: Home assessment without presentation 2

  • Proportion: 25%

  • Duration: -

  • Grouping: Individual

  • Grading scale: Letter (A - F)

  • Deadline for submission: Wednesday 09:00

Form of assessment: Home assessment without presentation 3

  • Proportion: 25%

  • Duration: -

  • Grouping: Individual

  • Grading scale: Letter (A - F)

  • Deadline for submission: Friday 09:00

 Form of assessment: Home assessment without presentation 4

  • Proportion: 25%

  • Duration: -

  • Grouping: Individual

  • Grading scale: Letter (A - F)

  • Deadline for submission: Saturday 09:00

Syllabus

  • Barabási, Albert-László, and Réka Albert. 1999. "Emergence of Scaling in Random Networks." Science 286 (5439):509-512. doi: 10.1126/science.286.5439.509.

  • Barthélemy, Marc. 2011. "Spatial networks." Physics Reports 499 (1):1-101. doi: https://doi.org/10.1016/j.physrep.2010.11.002.

  • Boccaletti, S., V. Latora, Y. Moreno, M. Chavez, and D. U. Hwang. 2006. "Complex networks: Structure and dynamics." Physics Reports 424 (4):175-308. doi: https://doi.org/10.1016/j.physrep.2005.10.009.

  • da F. Costa, Luciano, Francisco Rodrigues, Gonzalo Travieso, and Paulino Villas Boas. 2007. "Characterization of Complex Networks: A Survey of measurements." Advances in Physics 56:167-242. doi: 10.1080/00018730601170527.

  • Erdös, P. , and A. Rényi. 1960. "On the Evolution of Random Graphs." Publication of the Mathematical Institute of the Hungarian Academy of Sciences 5.

  • Fortunato, Santo. 2010. "Community detection in graphs." Physics Reports 486 (3):75-174. doi: https://doi.org/10.1016/j.physrep.2009.11.002.

Last updated from FS (Common Student System) Oct. 18, 2021 6:20:08 PM