Navigation

13M111ASM - Social Network Analysis

Course specification
Course title Social Network Analysis
Acronym 13M111ASM
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and communication technologies, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications
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 and complex network analysis from theory, methodology, and software aspects. Social and complex network analysis applications in different domains. Mathematical skills and software tools application in order to perform quantitative analysis of social and complex networks and their visualization.
The outcome Students will be able to: define research goals in the social and complex networks domain, obtain social network data in legal and ethical way, perform the formal modeling of the network, perform statistical and collaborative analysis of the networks using software tools, and interpret the results in accordance with the defined research goals.
Contents
Contents of lectures Social and complex network definition and graph representation. Data retrieval and network modeling. Basic network metrics, centrality measures; distance measures in networks; node role detection. Community detection and network clustering. Network models. Small world networks. Ego networks. Dynamic behavior of networks. Network visualization.
Contents of exercises Introduction to software tools and frameworks for social and complex network analysis and visualization: Gephi, UCINET, NetworkX. Data processing using Python. Data retrieval and transformation from real social networks, research paper databases, and web pages. Centrality measures and network visualization. Practical project.
Literature
  1. Barabási, Albert-László, Network science, Cambridge University Press, 2016. (Original title)
  2. Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside (Original title)
  3. D. Hansen, B. Shneiderman, M. Smith. 2010. Analyzing Social Media Networks with NodeXL: Insights from a Connected World. Morgan Kaufmann (Original title)
  4. Charles Kadushin, Understanding Social Networks: Theories, Concepts and Findings, Oxford University Press, 2012 (Original title)
  5. Christina Prell, Social Network Analysis: History, Theory and Methodology, SAGE Publications Ltd, 2012. (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