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Regular version of the site

Introduction to Network Analysis

Academic Year
Instruction in English
ECTS credits
Course type:
Compulsory course
1 year, 3, 4 module

Course Syllabus


This course is an introductory course in network analysis, designed to familiarize graduate students with the general concepts and basic techniques of network analysis in sociological re-search, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis, and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software. In addition, this course will provide ample opportu-nities to include network concepts in students’ master theses work.
Learning Objectives

Learning Objectives

  • The goal of the course is ensure that students understand topics and principles of network analsis.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know the basic principles of network analysis.
  • Know the advantages and disadvantages of various network analytic tools and methods.
  • Be able to explore the advantages and disadvantages of various network analytic tools and methods.
  • Be able to correctly selects appropriate model / method of network analysis for a given problem.
  • Know the major network modeling programs.
  • Be able to confidently uses available data to test proposed network hypotheses.
  • Be able to develop a solid network theoretical foundation for the project at hand.
  • Have the skill to processe learned information, and integrate learned material into a cohesive research toolchest.
  • Be able to integrate network information found from various sources and compensate for lack of data by adjusting models.
  • Be able to master advanced research methods, including network methods, without direct supervision, and is capable of using these methods to analyze complex models.
  • Have the skills to expresses network research ideas in English in written and oral communication.
  • Have the skills to effectively presents network research ideas to peers, instructors, and general audience.
Course Contents

Course Contents

  • Introduction
    Social network analysis: Methods or theory? Structural approach. Interdisciplinary interest in network analysis. Network theories most popular in sociology. Key network concepts: network, structure, nodes, ties, sociogram, structural and compositional variables, etc. Types of network data. Sampling and data collection in network analysis.
  • SNA methodology
    Survey instruments for collecting network data. Network data collection and ethical issues. Basic measures of network characteristics. Graphic representation of network relations.
  • SNA methodology II
    Network measures for dyads and triads. The forbidden triad. Clustering. Identifying tightly con-nected groups and subgroups in social networks. Small-world phenomenon. Homophily principal in personal relationships. Cultural and historical differences in network connectivity. Personal ties and social support.
  • SNA methodology III
    Centrality and Influence. Measures of Centrality. Two-mode networks: transformation, graphical representation, and analysis. Centrality and two-mode networks in the studies of power and in-fluence.
  • SNA models I
    The strength of weak ties. Social capital at the individuals and community level. Social capital in companies’ economic activities. Social capital in the labor market and its role in social mobility. Structural holes in competition.
  • SNA models II
    Social networks and education. Representation of mental models as social networks. Diffusion of innovation through social networks. Social networks and technology. Deviant behavior, crime and social networks. Social stratification, social change, and social networks.
  • Conclusion
    This session will bring all approaches to social network analysis together and apply it to the students’ current projects.
Assessment Elements

Assessment Elements

  • non-blocking Final Research Project
  • non-blocking Homework Assignments (5 x Varied points)
  • non-blocking In-Class Labs (9-10 x Varied points)
  • non-blocking Quizzes (Best 9 of 10, Varied points)
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.5 * Final Research Project + 0.2 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.1 * Quizzes (Best 9 of 10, Varied points)


Recommended Core Bibliography

  • Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and Methods in Social Network Analysis. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=132264
  • Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.
  • Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
  • Nooy, W. de, Mrvar, A., & Batagelj, V. (2005). Exploratory Social Network Analysis with Pajek. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138973

Recommended Additional Bibliography

  • Kadry, S., & Al-Taie, M. Z. (2014). Social Network Analysis : An Introduction with an Extensive Implementation to a Large-scale Online Network Using Pajek. Oak Park, IL: Bentham Science Publishers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=694016
  • Lazega, E., & Snijders, T. A. B. (2016). Multilevel Network Analysis for the Social Sciences : Theory, Methods and Applications. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1119294
  • Newman, M. (2010). Networks: An Introduction. Oxford University Press, 2010