13M111ASM - Social Network Analysis

Course specification
Course title		Social Network Analysis
Acronym		13M111ASM
Study programme		Electrical Engineering and Computing
Module
Type of study		master academic studies
Lecturer (for classes)		professor PhD Marko Mišić professor PhD Jelica Protić
Lecturer/Associate (for practice)		professor PhD Marko Mišić asst. MA Predrag Obradović, electrical and computer engineer
Lecturer/Associate (for OTC)		professor PhD Marko Mišić asst. MA Predrag Obradović, electrical and computer engineer
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
URL to the subject page		https://rti.etf.bg.edu.rs/rti/ms1asm/
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 and visualization: Gephi, UCINET, NodeXL, Pajek. Data processing using Python and R programming languages. Data retrieval and transformation from real social networks, research paper databases, and web pages. Centrality measures and network visualization. Practical project.
Literature
D. Hansen, B. Shneiderman, M. Smith. 2010. Analyzing Social Media Networks with NodeXL: Insights from a Connected World. Morgan Kaufmann (Original title) Charles Kadushin, Understanding Social Networks: Theories, Concepts and Findings, Oxford University Press, 2012 (Original title) Christina Prell, Social Network Analysis: History, Theory and Methodology, SAGE Publications Ltd, 2012. (Original title) 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