Study program / study programs: Advanced data analytics
Course: Introduction to complex networks theory 
Teacher(s): Marija Mitrović Dankulov, Aleksandra Alorić
Course status: Elective
ECTS points: 10
Prerequisites: none
Course objective:
Acquiring basic knowledge about complex networks, methods and tools for the quantitative analysis of their structure, and applications.  
Learning outcomes:
Students will acquire the basic concepts of theory of complex networks and be able to use various techniques of network analysis. Students will be able to map the data to various types of networks, do the statistical analysis of their structure and infer system properties based on the results of statistical analysis. 
Course structure and content:
Main course topics: Network representation of systems and basic network concepts (nodes, edges, adjacency matrix, temporal networks, multiplex networks, etc.) 
Concepts of global, mesoscopic and local network structure (degree, clustering, motifs, centrality measures, spectral properties of adjacency and laplacian matrix, community structure, etc.)
Statistical models of complex networks and their properties (Erdos-Renyi model, Barabasi-Alber model, Stochastic block model, Exponential random graphs)
Applications in biological systems
Application in social systems
Dynamical processes on networks
Introduction to networkx Python module
Introduction to complex networks and data mining
Literature/Readings:
M. E. J. Newman (2018), Networks: an introduction, Oxford University Press, ISBN: 978-0198805090
A. L. Barabasi (2015), Network Science, Cambridge University Press, ISBN: 978-1107076266. Available online: http://networksciencebook.com/
The number of class hours per week:
Lectures: 4
Labs: 0
Workshops: 0
Research study: 3
Other classes: 0
Teaching methods:
Individual and group work; lectures and labs
Evaluation/Grading (maximum 100 points):
Pre-exam requirements (Homework): 40
Final exam (Project: real-world application): 60