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Regular version of the site
Master 2020/2021

Social Network Analysis with R

Area of studies: Applied Mathematics and Informatics
When: 2 year, 3 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Valentina Kuskova
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 4

Course Syllabus


This course contains three independent, but interconnected components: 1. Theoretical: network theory and theory of networks, and their role in homological network of focal constructs of interest; 2. Methodological: methods of analysis and software programs used to analyze network data; 3. Applied: the theory and instruments learned in class are then used in individual and group work to design a research project in student’s own area of interest.
Learning Objectives

Learning Objectives

  • To develop and/or foster critical reviewing skills of published empirical research using network analytic methods.
  • To provide students with an understanding of the basic principles of network analysis and lay the foundation for future learning in the area.
  • To explore the advantages and disadvantages of various network analytic tools and methods, and demonstrate how they relate to other methods of analysis.
  • To develop student familiarity, through hands-on experience, with the major network modeling programs, so that they can use them and interpret their output.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know the interdisciplinary texts with the usage of language and apparatus of applied mathematics.
  • Be able to evaluate and reprocess methods and techniques of network analysis for a given problem.
  • Be able to analyze, verify, evaluate the completeness of information, can integrate information found from various sources and compensate for lack of data by adjusting models.
  • Be able to effectively solve problems in professional and social activities.
  • Be able to effectively generate new ideas and products in their professional activities.
  • Be able to create technological requirements and regulatory documents related to professional activities, describe them to others and control their implementation.
  • Be able to solve different tasks of professional activity, effectively communicate with experts from other areas.
  • Have a working skill on use of new methods and techniques of network analysis, additional packages and tools, without direct supervision.
Course Contents

Course Contents

  • Network Approach in Social Science Research
    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.
  • Network Data, Matrices, Graphic Representation of Social Networks, and Basic Network Measures
    Survey instruments for collecting network data. Network data collection and ethical issues. Basic measures of network characteristics. Graphic representation of network relations.
  • Personal Ties and Social Support
    Network measures for dyads and triads. The forbidden triad. Clustering. Identifying tightly connected 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.
  • Position Analysis in Social Networks
    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 influence.
  • Ties that Benefit
    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.
  • Social Networks in Society
    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.
  • Network models in R
    Readings and assignments will be handed out in class.
Assessment Elements

Assessment Elements

  • non-blocking Homework (5 total, varied points)
  • non-blocking In-class Labs (10 total, varied points)
  • non-blocking Quizzes (Best 9 out of 10, varied points)
  • non-blocking Course Project
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.5 * Course Project + 0.2 * Homework (5 total, varied points) + 0.2 * In-class Labs (10 total, varied points) + 0.1 * Quizzes (Best 9 out of 10, varied points)


Recommended Core Bibliography

  • Hans-Peter Kohler, Jere Behrman, & Susan Watkins. (2001). The density of social networks and fertility decisions: evidence from south nyanza district, kenya. Demography, (1), 43. https://doi.org/10.1353/dem.2001.0005
  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). BIRDS OF A FEATHER: Homophily in Social Networks. Annual Review of Sociology, 27, 415. https://doi.org/10.1146/annurev.soc.27.1.415
  • Nan Lin. (1999). Social Networks and Status Attainment. Annual Review of Sociology, 25, 467. https://doi.org/10.1146/annurev.soc.25.1.467
  • R. Pitts. (1979). The Medieval River Trade Network of Russia Revisited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.13ECA4E0
  • Uzzi, B., & Spiro, J. (2005). Collaboration and Creativity: The Small World Problem. American Journal of Sociology, 111(2), 447–504. https://doi.org/10.1086/432782

Recommended Additional Bibliography

  • Bat Batjargal. (2007). Network triads: transitivity, referral and venture capital decisions in China and Russia. Journal of International Business Studies, (6), 998. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.pal.jintbs.v38y2007i6p998.1012
  • Gerlach, M. L. (1992). The Japanese Corporate Network: A Blockmodel Analysis. Administrative Science Quarterly, 37(1), 105–139. https://doi.org/10.2307/2393535
  • Gibson, J. L. (2001). Social Networks, Civil Society, and the Prospects for Consolidating Russia’s Democratic Transition. American Journal of Political Science (Wiley-Blackwell), 45(1), 51. https://doi.org/10.2307/2669359
  • Guseva, A., & Rona-Tas, A. (2001). Uncertainty, Risk, and Trust: Russian and American Credit Card Markets Compared. American Sociological Review, 66(5), 623–646. https://doi.org/10.2307/3088951
  • Marsden, P. V. (1987). Core Discussion Networks of Americans. American Sociological Review, 52(1), 122–131. https://doi.org/10.2307/2095397
  • McPherson, M., Smith-Lovin, L., & Brashears, M. E. (2006). Social Isolation in America: Changes in Core Discussion Networks over Two Decades. American Sociological Review, 71(3), 353–375. https://doi.org/10.1177/000312240607100301
  • Mizruchi, M. S. (1996). WHAT DO INTERLOCKS DO? An Analysis, Critique, and Assessment of Research on Interlocking Directories. Annual Review of Sociology, 22, 271. https://doi.org/10.1146/annurev.soc.22.1.271