13M111ASM - Social Network Analysis

Course specification
Course title Social Network Analysis
Acronym 13M111ASM
Study programme Electrical Engineering and Computing
Module Software Engineering
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition no additional prerequisite
The goal Concepts of social network analysis from theory, methodology, and software aspects. Social network analysis applications: real social networks on the Internet, such as Facebook, LinkedIn, and Twitter, co-authorship and citation networks in scientific production. Mathematical skills and software tools application in order to perform quantitative analysis of social networks and their visualization.
The outcome Students will be able to: define research goals in the social networks domain, obtain social network data in legal and ethical way, perform the formal modeling of the network and its actors, perform statistical and collaborative analysis of the networks using software tools, and interpret the results in accordance witt the defined research goals.
Contents of lectures Social network definition and graph representation. Data retrieval and representation; network modeling and choice of directed, undirected, and weighted graphs. Basic network metrics, centrality measures; distance measures in networks; node role detection. Community detection and network clustering. Small world networks. Ego networks. Dynamic behavior of networks. Network visualization.
Contents of exercises Introduction to software tools for social network analysis: Gephi, UCINET, NodeXL, Pajek. Language for statistical analysis R. Data retrieval and transformation from real social networks, research paper databases, and web pages. Centrality measures and network visualization. Practical project.
  1. D. Hansen, B. Shneiderman, M. Smith. 2010. Analyzing Social Media Networks with NodeXL: Insights from a Connected World. Morgan Kaufmann (Original title)
  2. Charles Kadushin, Understanding Social Networks: Theories, Concepts and Findings, Oxford University Press, 2012 (Original title)
  3. Christina Prell, Social Network Analysis: History, Theory and Methodology, SAGE Publications Ltd, 2012. (Original title)
  4. Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside (Original title)
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
2 2 1
Methods of teaching Lectures, data retrieval and analysis, case study, interpretation of results.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 40
Practical lessons 0 Oral examination 0
Projects 60
Colloquia 0
Seminars 0