| Study program / study programs: Advanced data analytics |
| Course: Social Network Analysis |
| Teacher(s): Jelena Jovanović, Aleksandra Alorić, Marija Mitrović Dankulov |
| Course status: Elective |
| ECTS points: 10 |
| Prerequisites: none |
| Course objective: To guide and assist students in: – learning about main concepts, methods, and techniques of social network analysis (SNA)- developing a solid understanding of i) the kinds of analytical questions and/or problems that can be dealt with using the SNA approach; ii) pros and cons of individual SNA methods and techniques, as to be able to select appropriate SNA methods / techniques for a particular problem / question- acquiring practical skills in the analysis of network data, using publicly available SNA software tools and datasets. |
| Learning outcomes: Students will develop a solid understanding of main SNA concepts, methods, and techniques. They will also get an insight into the potentials and limitations of these methods and techniques, and thus be able to choose appropriate one(s) for a particular application case. Furthermore, they will acquire practical skills in using SNA software tools for doing network analysis with real-world datasets. |
| Course structure and content: Main course topics: Graph-based data representation (nodes, edges, adjacency matrix, etc.) Network features (degree distribution, connectedness, transitivity, etc.) Centrality measures (degree centrality, betweenness centrality, eigen vector centrality, etc) Communities in a network. Community detection Statistical models of network formation (e.g. ERGMs) Diffusion of information and innovation through a network. All course topics will be introduced through practical work with publicly available software libraries for SNA (e.g., R or Python SNA packages) and real-world network datasets. The practical work will also include network visualization, as well as data collection and preparation for network analysis. |
| Literature/Readings: Selected chapters from the following books: M. Tsvetovat and A. Kouznetsov. 2011. Social Network Analysis for Startups: Finding connections on the social web. O’Reilly Media Inc., Sebastopol, CA, US A.D. Easley and J. Kleinberg. 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, New York, NY, USA. |
| The number of class hours per week: Lectures: 4 Labs: 0 Workshops: 0 Research study: 3 Other classes: 0 |
| Teaching methods: Lectures will introduce main concepts for each course topic, and will include a lot of practical work with the topic-specific software libraries. Research study will be fully practical, based on individual and group work. |
| Evaluation/Grading (maximum 100 points): Pre-exam requirements (Project: simple application case): 40 Final exam (Project: real-world application case): 60 |
