Course type: elective
Lecturer: Vladimir Batagelj, Ph.D., Full Professor
 

For the timeline see Curriculum.

Prerequisites:

• Enrollment into the program.

Content (Syllabus outline):

• Introduction, basic notions
• Sources of network data
• Quality of network measurement
• Types of networks, software for network analysis
• Structure of networks: connectivities, partitions, components, cores, reductions, patterns, skeletons
• Measures of centrality and importance, islands
• Markov chains as networks
• Acyclic networks
• Two-mode networks and network multiplication
• Clustering in networks and block modeling
• Statistical analysis and models of networks, scale-free networks
• Applications: genealogies, internet, text analysis, bibliometrics, ...

Objectives and competences:
The goal of the course is to introduce the basic concepts and methods of network analysis, and to enable the students to perform analyses of network data by themselves.

Intended learning outcomes:
Knowledge and understanding:

• Understanding of basic concepts and methods of network analysis.
• Ability to select the right methods for network analysis tasks and perform them using an appropriate software tool.
• Ability to interpret the obtained results according to theoretical background; new views on the problem.
• Ability to combine the network analysis with other data analysis methods.